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Volume 162: International Conference on Machine Learning, 17-23 July 2022, Baltimore, Maryland, USA

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Editors: Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, Sivan Sabato

[bib][citeproc]

PAC-Bayesian Bounds on Rate-Efficient Classifiers

Alhabib Abbas, Yiannis Andreopoulos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1-9

Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning

Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, Tianyi Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10-32

An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn

Emmanuel Abbe, Elisabetta Cornacchia, Jan Hazla, Christopher Marquis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:33-52

Active Sampling for Min-Max Fairness

Jacob D Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:53-65

Meaningfully debugging model mistakes using conceptual counterfactual explanations

Abubakar Abid, Mert Yuksekgonul, James Zou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:66-88

Batched Dueling Bandits

Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:89-110

Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.

Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:111-135

Deep equilibrium networks are sensitive to initialization statistics

Atish Agarwala, Samuel S Schoenholz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:136-160

Learning of Cluster-based Feature Importance for Electronic Health Record Time-series

Henrique Aguiar, Mauro Santos, Peter Watkinson, Tingting Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:161-179

On the Convergence of the Shapley Value in Parametric Bayesian Learning Games

Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:180-196

Individual Preference Stability for Clustering

Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:197-246

Understanding the unstable convergence of gradient descent

Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:247-257

Minimum Cost Intervention Design for Causal Effect Identification

Sina Akbari, Jalal Etesami, Negar Kiyavash; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:258-289

How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models

Ahmed Alaa, Boris Van Breugel, Evgeny S. Saveliev, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:290-306

A Natural Actor-Critic Framework for Zero-Sum Markov Games

Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:307-366

Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations

Mohammad Mahmudul Alam, Edward Raff, Tim Oates, James Holt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:367-393

Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer

Lucas Nunes Alegre, Ana Bazzan, Bruno C. Da Silva; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:394-413

Structured Stochastic Gradient MCMC

Antonios Alexos, Alex J Boyd, Stephan Mandt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:414-434

XAI for Transformers: Better Explanations through Conservative Propagation

Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:435-451

RUMs from Head-to-Head Contests

Matteo Almanza, Flavio Chierichetti, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:452-467

Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval

Uri Alon, Frank Xu, Junxian He, Sudipta Sengupta, Dan Roth, Graham Neubig; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:468-485

Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees

Verónica Álvarez, Santiago Mazuelas, Jose A Lozano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:486-499

Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation

Sebastian E Ament, Carla P Gomes; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:500-516

Public Data-Assisted Mirror Descent for Private Model Training

Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, Abhradeep Thakurta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:517-535

On Last-Iterate Convergence Beyond Zero-Sum Games

Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina, Tuomas Sandholm; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:536-581

Online Algorithms with Multiple Predictions

Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:582-598

Learning to Hash Robustly, Guaranteed

Alexandr Andoni, Daniel Beaglehole; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:599-618

Set Based Stochastic Subsampling

Bruno Andreis, Seanie Lee, A. Tuan Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:619-638

Towards Understanding Sharpness-Aware Minimization

Maksym Andriushchenko, Nicolas Flammarion; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:639-668

Fair and Fast k-Center Clustering for Data Summarization

Haris Angelidakis, Adam Kurpisz, Leon Sering, Rico Zenklusen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:669-702

Interactive Correlation Clustering with Existential Cluster Constraints

Rico Angell, Nicholas Monath, Nishant Yadav, Andrew Mccallum; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:703-716

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

Anastasios N Angelopoulos, Amit Pal Kohli, Stephen Bates, Michael Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:717-730

AdaGrad Avoids Saddle Points

Kimon Antonakopoulos, Panayotis Mertikopoulos, Georgios Piliouras, Xiao Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:731-771

UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

Kimon Antonakopoulos, Dong Quan Vu, Volkan Cevher, Kfir Levy, Panayotis Mertikopoulos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:772-795

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning

Javier Antoran, David Janz, James U Allingham, Erik Daxberger, Riccardo Rb Barbano, Eric Nalisnick, Jose Miguel Hernandez-Lobato; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:796-821

EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning

Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:822-843

Online Balanced Experimental Design

David Arbour, Drew Dimmery, Tung Mai, Anup Rao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:844-864

VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty

Randy Ardywibowo, Zepeng Huo, Zhangyang Wang, Bobak J Mortazavi, Shuai Huang, Xiaoning Qian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:865-877

Thresholded Lasso Bandit

Kaito Ariu, Kenshi Abe, Alexandre Proutiere; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:878-928

Gradient Based Clustering

Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic, Soummya Kar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:929-947

Understanding Gradient Descent on the Edge of Stability in Deep Learning

Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:948-1024

Private optimization in the interpolation regime: faster rates and hardness results

Hilal Asi, Karan Chadha, Gary Cheng, John Duchi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1025-1045

Optimal Algorithms for Mean Estimation under Local Differential Privacy

Hilal Asi, Vitaly Feldman, Kunal Talwar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1046-1056

Asymptotically-Optimal Gaussian Bandits with Side Observations

Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1057-1077

Congested Bandits: Optimal Routing via Short-term Resets

Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1078-1100

Do More Negative Samples Necessarily Hurt In Contrastive Learning?

Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1101-1116

H-Consistency Bounds for Surrogate Loss Minimizers

Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1117-1174

Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime

Kyriakos Axiotis, Maxim Sviridenko; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1175-1197

Proving Theorems using Incremental Learning and Hindsight Experience Replay

Eser Aygün, Ankit Anand, Laurent Orseau, Xavier Glorot, Stephen M Mcaleer, Vlad Firoiu, Lei M Zhang, Doina Precup, Shibl Mourad; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1198-1210

Near-optimal rate of consistency for linear models with missing values

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1211-1243

How Tempering Fixes Data Augmentation in Bayesian Neural Networks

Gregor Bachmann, Lorenzo Noci, Thomas Hofmann; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1244-1260

ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD

Karl Bäckström, Marina Papatriantafilou, Philippas Tsigas; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1261-1276

From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model

Heesun Bae, Seungjae Shin, Byeonghu Na, Joonho Jang, Kyungwoo Song, Il-Chul Moon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1277-1297

data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language

Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1298-1312

End-to-End Balancing for Causal Continuous Treatment-Effect Estimation

Taha Bahadori, Eric Tchetgen Tchetgen, David Heckerman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1313-1326

A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs

Lu Bai, Lixin Cui, Hancock Edwin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1327-1336

Near-Optimal Learning of Extensive-Form Games with Imperfect Information

Yu Bai, Chi Jin, Song Mei, Tiancheng Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1337-1382

Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification

Junwen Bai, Shufeng Kong, Carla P Gomes; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1383-1398

A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing

He Bai, Renjie Zheng, Junkun Chen, Mingbo Ma, Xintong Li, Liang Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1399-1411

Stability Based Generalization Bounds for Exponential Family Langevin Dynamics

Arindam Banerjee, Tiancong Chen, Xinyan Li, Yingxue Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1412-1449

Certified Neural Network Watermarks with Randomized Smoothing

Arpit Bansal, Ping-Yeh Chiang, Michael J Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P Dickerson, Tom Goldstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1450-1465

Data Scaling Laws in NMT: The Effect of Noise and Architecture

Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1466-1482

Learning Stable Classifiers by Transferring Unstable Features

Yujia Bao, Shiyu Chang, Dr.Regina Barzilay; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1483-1507

Fast Composite Optimization and Statistical Recovery in Federated Learning

Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1508-1536

Generative Modeling for Multi-task Visual Learning

Zhipeng Bao, Martial Hebert, Yu-Xiong Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1537-1554

Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models

Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1555-1584

On the Surrogate Gap between Contrastive and Supervised Losses

Han Bao, Yoshihiro Nagano, Kento Nozawa; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1585-1606

Representation Topology Divergence: A Method for Comparing Neural Network Representations.

Serguei Barannikov, Ilya Trofimov, Nikita Balabin, Evgeny Burnaev; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1607-1626

Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation

Adarsh Barik, Jean Honorio; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1627-1646

Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time

Burak Bartan, Mert Pilanci; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1647-1663

Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games

Lucas Baudin, Rida Laraki; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1664-1690

Information Discrepancy in Strategic Learning

Yahav Bechavod, Chara Podimata, Steven Wu, Juba Ziani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1691-1715

On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces

Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian M Sadler, Pratap Tokekar, Alec Koppel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1716-1731

Imitation Learning by Estimating Expertise of Demonstrators

Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1732-1748

Matching Normalizing Flows and Probability Paths on Manifolds

Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximillian Nickel, Aditya Grover, Ricky T. Q. Chen, Yaron Lipman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1749-1763

Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models

Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1764-1786

Neural Inverse Kinematic

Raphael Bensadoun, Shir Gur, Nitsan Blau, Lior Wolf; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1787-1797

Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

Gregory Benton, Wesley Maddox, Andrew Gordon Wilson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1798-1816

Gradient Descent on Neurons and its Link to Approximate Second-order Optimization

Frederik Benzing; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1817-1853

Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints

Martino Bernasconi, Federico Cacciamani, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti, Francesco Trovò; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1854-1873

Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

Peter Bevan, Amir Atapour-Abarghouei; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1874-1892

Approximate Bayesian Computation with Domain Expert in the Loop

Ayush Bharti, Louis Filstroff, Samuel Kaski; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1893-1905

Minimax M-estimation under Adversarial Contamination

Sujay Bhatt, Guanhua Fang, Ping Li, Gennady Samorodnitsky; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1906-1924

Nearly Optimal Catoni’s M-estimator for Infinite Variance

Sujay Bhatt, Guanhua Fang, Ping Li, Gennady Samorodnitsky; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1925-1944

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning

Alberto Bietti, Chen-Yu Wei, Miroslav Dudik, John Langford, Steven Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1945-1962

Non-Vacuous Generalisation Bounds for Shallow Neural Networks

Felix Biggs, Benjamin Guedj; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1963-1981

Structure-preserving GANs

Jeremiah Birrell, Markos Katsoulakis, Luc Rey-Bellet, Wei Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1982-2020

Scalable Spike-and-Slab

Niloy Biswas, Lester Mackey, Xiao-Li Meng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2021-2040

Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities

Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2041-2074

A query-optimal algorithm for finding counterfactuals

Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2075-2090

Popular decision tree algorithms are provably noise tolerant

Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2091-2106

Optimizing Sequential Experimental Design with Deep Reinforcement Learning

Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2107-2128

Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)

Huang Bojun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2129-2159

Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model

Heejong Bong, Alessandro Rinaldo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2160-2177

How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective

Akhilan Boopathy, Ila Fiete; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2178-2205

Improving Language Models by Retrieving from Trillions of Tokens

Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego De Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack Rae, Erich Elsen, Laurent Sifre; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2206-2240

Lie Point Symmetry Data Augmentation for Neural PDE Solvers

Johannes Brandstetter, Max Welling, Daniel E Worrall; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2241-2256

An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees

Guillaume Braun, Hemant Tyagi, Christophe Biernacki; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2257-2291

Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems

Manuel Brenner, Florian Hess, Jonas M Mikhaeil, Leonard F Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2292-2320

Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters

Luc Brogat-Motte, Rémi Flamary, Celine Brouard, Juho Rousu, Florence D’Alché-Buc; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2321-2335

Efficient Learning of CNNs using Patch Based Features

Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Schwartz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2336-2356

Causal structure-based root cause analysis of outliers

Kailash Budhathoki, Lenon Minorics, Patrick Bloebaum, Dominik Janzing; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2357-2369

IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages

Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer, Siva Reddy, Desmond Elliott, Edoardo Maria Ponti, Ivan Vulić; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2370-2392

Interactive Inverse Reinforcement Learning for Cooperative Games

Thomas Kleine Büning, Anne-Marie George, Christos Dimitrakakis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2393-2413

Convolutional and Residual Networks Provably Contain Lottery Tickets

Rebekka Burkholz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2414-2433

Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path

Haoyuan Cai, Tengyu Ma, Simon Du; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2434-2456

Convergence of Invariant Graph Networks

Chen Cai, Yusu Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2457-2484

Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency

Qi Cai, Zhuoran Yang, Zhaoran Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2485-2522

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times

Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2523-2541

Adaptive Gaussian Process Change Point Detection

Edoardo Caldarelli, Philippe Wenk, Stefan Bauer, Andreas Krause; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2542-2571

Measuring dissimilarity with diffeomorphism invariance

Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2572-2596

A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling

Yiting Cao, Chao Lan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2597-2608

Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications

Alexandre Capone, Armin Lederer, Sandra Hirche; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2609-2624

Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning

Cristiano Capone, Cosimo Lupo, Paolo Muratore, Pier Stanislao Paolucci; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2625-2637

A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving

Luca Carminati, Federico Cacciamani, Marco Ciccone, Nicola Gatti; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2638-2657

RECAPP: Crafting a More Efficient Catalyst for Convex Optimization

Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2658-2685

Estimating and Penalizing Induced Preference Shifts in Recommender Systems

Micah D Carroll, Anca Dragan, Stuart Russell, Dylan Hadfield-Menell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2686-2708

YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone

Edresson Casanova, Julian Weber, Christopher D Shulby, Arnaldo Candido Junior, Eren Gölge, Moacir A Ponti; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2709-2720

The Infinite Contextual Graph Markov Model

Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2721-2737

Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data

Timothy J Castiglia, Anirban Das, Shiqiang Wang, Stacy Patterson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2738-2766

Online Learning with Knapsacks: the Best of Both Worlds

Matteo Castiglioni, Andrea Celli, Christian Kroer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2767-2783

Stabilizing Off-Policy Deep Reinforcement Learning from Pixels

Edoardo Cetin, Philip J Ball, Stephen Roberts, Oya Celiktutan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2784-2810

Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization

Karan Chadha, Gary Cheng, John Duchi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2811-2827

Robust Imitation Learning against Variations in Environment Dynamics

Jongseong Chae, Seungyul Han, Whiyoung Jung, Myungsik Cho, Sungho Choi, Youngchul Sung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2828-2852

Fairness with Adaptive Weights

Junyi Chai, Xiaoqian Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2853-2866

UNIREX: A Unified Learning Framework for Language Model Rationale Extraction

Aaron Chan, Maziar Sanjabi, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren, Hamed Firooz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2867-2889

Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?

Keshigeyan Chandrasegaran, Ngoc-Trung Tran, Yunqing Zhao, Ngai-Man Cheung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2890-2916

Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models

Jen-Hao Rick Chang, Ashish Shrivastava, Hema Koppula, Xiaoshuai Zhang, Oncel Tuzel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2917-2937

Learning Bellman Complete Representations for Offline Policy Evaluation

Jonathan Chang, Kaiwen Wang, Nathan Kallus, Wen Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2938-2971

Sample Efficient Learning of Predictors that Complement Humans

Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, Samira Samadi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2972-3005

Nyström Kernel Mean Embeddings

Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3006-3024

Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets

Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wang, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3025-3039

Learning Domain Adaptive Object Detection with Probabilistic Teacher

Meilin Chen, Weijie Chen, Shicai Yang, Jie Song, Xinchao Wang, Lei Zhang, Yunfeng Yan, Donglian Qi, Yueting Zhuang, Di Xie, Shiliang Pu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3040-3055

The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning

Wei-Ning Chen, Christopher A Choquette Choo, Peter Kairouz, Ananda Theertha Suresh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3056-3089

Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning

Mayee Chen, Daniel Y Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, Christopher Re; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3090-3122

Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk

Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3123-3148

On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs

Yuanzhou Chen, Jiafan He, Quanquan Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3149-3183

Streaming Algorithms for Support-Aware Histograms

Justin Chen, Piotr Indyk, Tal Wagner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3184-3203

Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP

Liyu Chen, Rahul Jain, Haipeng Luo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3204-3245

Learning Infinite-horizon Average-reward Markov Decision Process with Constraints

Liyu Chen, Rahul Jain, Haipeng Luo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3246-3270

Active Multi-Task Representation Learning

Yifang Chen, Kevin Jamieson, Simon Du; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3271-3298

On Collective Robustness of Bagging Against Data Poisoning

Ruoxin Chen, Zenan Li, Jie Li, Junchi Yan, Chentao Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3299-3319

Online Active Regression

Cheng Chen, Yi Li, Yiming Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3320-3335

Selling Data To a Machine Learner: Pricing via Costly Signaling

Junjie Chen, Minming Li, Haifeng Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3336-3359

ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases

Jintai Chen, Kuanlun Liao, Kun Wei, Haochao Ying, Danny Z Chen, Jian Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3360-3370

Weisfeiler-Lehman Meets Gromov-Wasserstein

Samantha Chen, Sunhyuk Lim, Facundo Memoli, Zhengchao Wan, Yusu Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3371-3416

On Non-local Convergence Analysis of Deep Linear Networks

Kun Chen, Dachao Lin, Zhihua Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3417-3443

Flow-based Recurrent Belief State Learning for POMDPs

Xiaoyu Chen, Yao Mark Mu, Ping Luo, Shengbo Li, Jianyu Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3444-3468

Structure-Aware Transformer for Graph Representation Learning

Dexiong Chen, Leslie O’Bray, Karsten Borgwardt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3469-3489

The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation

Wei-Ning Chen, Ayfer Ozgur, Peter Kairouz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3490-3506

Learning Mixtures of Linear Dynamical Systems

Yanxi Chen, H. Vincent Poor; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3507-3557

On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation

Xiaohong Chen, Zhengling Qi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3558-3582

Faster Fundamental Graph Algorithms via Learned Predictions

Justin Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3583-3602

Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters

Xin Chen, Yujie Tang, Na Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3603-3620

Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection

Wenchao Chen, Long Tian, Bo Chen, Liang Dai, Zhibin Duan, Mingyuan Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3621-3633

Auxiliary Learning with Joint Task and Data Scheduling

Hong Chen, Xin Wang, Chaoyu Guan, Yue Liu, Wenwu Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3634-3647

Optimization-Induced Graph Implicit Nonlinear Diffusion

Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3648-3661

Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile

Dong Chen, Lingfei Wu, Siliang Tang, Xiao Yun, Bo Long, Yueting Zhuang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3662-3678

Adaptive Model Design for Markov Decision Process

Siyu Chen, Donglin Yang, Jiayang Li, Senmiao Wang, Zhuoran Yang, Zhaoran Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3679-3700

State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks

Yanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tiejun Huang, Yonghong Tian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3701-3715

Efficient Online ML API Selection for Multi-Label Classification Tasks

Lingjiao Chen, Matei Zaharia, James Zou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3716-3746

Data-Efficient Double-Win Lottery Tickets from Robust Pre-training

Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3747-3759

Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness

Tianlong Chen, Huan Zhang, Zhenyu Zhang, Shiyu Chang, Sijia Liu, Pin-Yu Chen, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3760-3772

Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation

Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3773-3793

Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis

Ziyi Chen, Yi Zhou, Rong-Rong Chen, Shaofeng Zou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3794-3834

Task-aware Privacy Preservation for Multi-dimensional Data

Jiangnan Cheng, Ao Tang, Sandeep Chinchali; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3835-3851

Adversarially Trained Actor Critic for Offline Reinforcement Learning

Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3852-3878

Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra

Nadiia Chepurko, Kenneth Clarkson, Lior Horesh, Honghao Lin, David Woodruff; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3879-3900

RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests

Victor Chernozhukov, Whitney Newey, Vı́ctor M Quintas-Martı́nez, Vasilis Syrgkanis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3901-3914

Self-supervised learning with random-projection quantizer for speech recognition

Chung-Cheng Chiu, James Qin, Yu Zhang, Jiahui Yu, Yonghui Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3915-3924

Discrete Probabilistic Inverse Optimal Transport

Wei-Ting Chiu, Pei Wang, Patrick Shafto; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3925-3946

Selective Network Linearization for Efficient Private Inference

Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, Chinmay Hegde; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3947-3961

From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers

Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamas Sarlos, Adrian Weller, Thomas Weingarten; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3962-3983

Shuffle Private Linear Contextual Bandits

Sayak Ray Chowdhury, Xingyu Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3984-4009

DNA: Domain Generalization with Diversified Neural Averaging

Xu Chu, Yujie Jin, Wenwu Zhu, Yasha Wang, Xin Wang, Shanghang Zhang, Hong Mei; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4010-4034

TPC: Transformation-Specific Smoothing for Point Cloud Models

Wenda Chu, Linyi Li, Bo Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4035-4056

Unified Scaling Laws for Routed Language Models

Aidan Clark, Diego De Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake Hechtman, Trevor Cai, Sebastian Borgeaud, George Bm Van Den Driessche, Eliza Rutherford, Tom Hennigan, Matthew J Johnson, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Marc’Aurelio Ranzato, Jack Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4057-4086

Context-Aware Drift Detection

Oliver Cobb, Arnaud Van Looveren; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4087-4111

On the Robustness of CountSketch to Adaptive Inputs

Edith Cohen, Xin Lyu, Jelani Nelson, Tamas Sarlos, Moshe Shechner, Uri Stemmer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4112-4140

Diffusion bridges vector quantized variational autoencoders

Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4141-4156

Online and Consistent Correlation Clustering

Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4157-4179

Massively Parallel $k$-Means Clustering for Perturbation Resilient Instances

Vincent Cohen-Addad, Vahab Mirrokni, Peilin Zhong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4180-4201

One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams

Benjamin Coleman, Benito Geordie, Li Chou, R. A. Leo Elworth, Todd Treangen, Anshumali Shrivastava; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4202-4218

Transfer and Marginalize: Explaining Away Label Noise with Privileged Information

Mark Collier, Rodolphe Jenatton, Effrosyni Kokiopoulou, Jesse Berent; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4219-4237

MAML and ANIL Provably Learn Representations

Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4238-4310

Entropic Causal Inference: Graph Identifiability

Spencer Compton, Kristjan Greenewald, Dmitriy A Katz, Murat Kocaoglu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4311-4343

Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model

Jean-Rémy Conti, Nathan Noiry, Stephan Clemencon, Vincent Despiegel, Stéphane Gentric; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4344-4369

Counterfactual Transportability: A Formal Approach

Juan D Correa, Sanghack Lee, Elias Bareinboim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4370-4390

Label-Free Explainability for Unsupervised Models

Jonathan Crabbé, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4391-4420

Evaluating the Adversarial Robustness of Adaptive Test-time Defenses

Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, Taylan Cemgil; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4421-4435

Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers

Francesco Croce, Matthias Hein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4436-4454

Self-conditioning Pre-Trained Language Models

Xavier Suau Cuadros, Luca Zappella, Nicholas Apostoloff; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4455-4473

Only tails matter: Average-Case Universality and Robustness in the Convex Regime

Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4474-4491

Principal Component Flows

Edmond Cunningham, Adam D Cobb, Susmit Jha; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4492-4519

Deep symbolic regression for recurrence prediction

Stéphane D’Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, Francois Charton; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4520-4536

Continuous Control with Action Quantization from Demonstrations

Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4537-4557

Dialog Inpainting: Turning Documents into Dialogs

Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Y Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4558-4586

DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training

Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, Dacheng Tao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4587-4604

Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization

Hanjun Dai, Mengjiao Yang, Yuan Xue, Dale Schuurmans, Bo Dai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4605-4617

Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning

Angelo Damiani, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4618-4629

Understanding Robust Generalization in Learning Regular Languages

Soham Dan, Osbert Bastani, Dan Roth; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4630-4643

Unsupervised Image Representation Learning with Deep Latent Particles

Tal Daniel, Aviv Tamar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4644-4665

Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation

Chris Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4666-4689

Monarch: Expressive Structured Matrices for Efficient and Accurate Training

Tri Dao, Beidi Chen, Nimit S Sohoni, Arjun Desai, Michael Poli, Jessica Grogan, Alexander Liu, Aniruddh Rao, Atri Rudra, Christopher Re; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4690-4721

Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems

Giannis Daras, Yuval Dagan, Alex Dimakis, Constantinos Daskalakis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4722-4753

Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing

Mohammad Zalbagi Darestani, Jiayu Liu, Reinhard Heckel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4754-4776

Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, Andrew Mccallum; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4777-4793

Framework for Evaluating Faithfulness of Local Explanations

Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4794-4815

Distinguishing rule and exemplar-based generalization in learning systems

Ishita Dasgupta, Erin Grant, Tom Griffiths; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4816-4830

Robust Multi-Objective Bayesian Optimization Under Input Noise

Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4831-4866

Attentional Meta-learners for Few-shot Polythetic Classification

Ben J Day, Ramon Viñas Torné, Nikola Simidjievski, Pietro Lió; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4867-4889

Adversarial Vulnerability of Randomized Ensembles

Hassan Dbouk, Naresh Shanbhag; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4890-4917

Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization

Giuseppe Bruno De Luca, Eva Silverstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4918-4936

Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass

Giorgia Dellaferrera, Gabriel Kreiman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4937-4955

DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

Fei Deng, Ingook Jang, Sungjin Ahn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4956-4975

NeuralEF: Deconstructing Kernels by Deep Neural Networks

Zhijie Deng, Jiaxin Shi, Jun Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4976-4992

Deep Causal Metric Learning

Xiang Deng, Zhongfei Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4993-5006

On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming

Gregory Dexter, Agniva Chowdhury, Haim Avron, Petros Drineas; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5007-5038

Analysis of Stochastic Processes through Replay Buffers

Shirli Di-Castro, Shie Mannor, Dotan Di Castro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5039-5060

Streaming Algorithms for High-Dimensional Robust Statistics

Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5061-5117

Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5118-5141

Variational Feature Pyramid Networks

Panagiotis Dimitrakopoulos, Giorgos Sfikas, Christophoros Nikou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5142-5152

Understanding Doubly Stochastic Clustering

Tianjiao Ding, Derek Lim, Rene Vidal, Benjamin D Haeffele; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5153-5165

Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence

Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Mihailo Jovanovic; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5166-5220

Generalization and Robustness Implications in Object-Centric Learning

Andrea Dittadi, Samuele S Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5221-5285

Fair Generalized Linear Models with a Convex Penalty

Hyungrok Do, Preston Putzel, Axel S Martin, Padhraic Smyth, Judy Zhong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5286-5308

Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense

Bao Gia Doan, Ehsan M Abbasnejad, Javen Qinfeng Shi, Damith Ranasinghe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5309-5323

On the Adversarial Robustness of Causal Algorithmic Recourse

Ricardo Dominguez-Olmedo, Amir H Karimi, Bernhard Schölkopf; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5324-5342

Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks

Runpei Dong, Zhanhong Tan, Mengdi Wu, Linfeng Zhang, Kaisheng Ma; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5343-5359

PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs

Zehao Dong, Muhan Zhang, Fuhai Li, Yixin Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5360-5377

Privacy for Free: How does Dataset Condensation Help Privacy?

Tian Dong, Bo Zhao, Lingjuan Lyu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5378-5396

Fast rates for noisy interpolation require rethinking the effect of inductive bias

Konstantin Donhauser, Nicolò Ruggeri, Stefan Stojanovic, Fanny Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5397-5428

Adapting to Mixing Time in Stochastic Optimization with Markovian Data

Ron Dorfman, Kfir Yehuda Levy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5429-5446

TACTiS: Transformer-Attentional Copulas for Time Series

Alexandre Drouin, Étienne Marcotte, Nicolas Chapados; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5447-5493

Branching Reinforcement Learning

Yihan Du, Wei Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5494-5530

Bayesian Imitation Learning for End-to-End Mobile Manipulation

Yuqing Du, Daniel Ho, Alex Alemi, Eric Jang, Mohi Khansari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5531-5546

GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P Bosma, Zongwei Zhou, Tao Wang, Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc Le, Yonghui Wu, Zhifeng Chen, Claire Cui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5547-5569

Learning Iterative Reasoning through Energy Minimization

Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5570-5582

SE(3) Equivariant Graph Neural Networks with Complete Local Frames

Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, Tie-Yan Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5583-5608

A Context-Integrated Transformer-Based Neural Network for Auction Design

Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, Xiaotie Deng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5609-5626

Augment with Care: Contrastive Learning for Combinatorial Problems

Haonan Duan, Pashootan Vaezipoor, Max B Paulus, Yangjun Ruan, Chris Maddison; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5627-5642

Parametric Visual Program Induction with Function Modularization

Xuguang Duan, Xin Wang, Ziwei Zhang, Wenwu Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5643-5658

Bayesian Deep Embedding Topic Meta-Learner

Zhibin Duan, Yishi Xu, Jianqiao Sun, Bo Chen, Wenchao Chen, Chaojie Wang, Mingyuan Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5659-5670

Deletion Robust Submodular Maximization over Matroids

Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5671-5693

From data to functa: Your data point is a function and you can treat it like one

Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Jimenez Rezende, Dan Rosenbaum; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5694-5725

Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models

Valentin Durante, George Katsirelos, Thomas Schiex; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5726-5741

Robust Counterfactual Explanations for Tree-Based Ensembles

Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5742-5756

On the Difficulty of Defending Self-Supervised Learning against Model Extraction

Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5757-5776

LIMO: Latent Inceptionism for Targeted Molecule Generation

Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael Gilson, Rose Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5777-5792

Inductive Biases and Variable Creation in Self-Attention Mechanisms

Benjamin L Edelman, Surbhi Goel, Sham Kakade, Cyril Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5793-5831

Provable Reinforcement Learning with a Short-Term Memory

Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5832-5850

Sparsity in Partially Controllable Linear Systems

Yonathan Efroni, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5851-5860

FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning

Anis Elgabli, Chaouki Ben Issaid, Amrit Singh Bedi, Ketan Rajawat, Mehdi Bennis, Vaneet Aggarwal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5861-5877

pathGCN: Learning General Graph Spatial Operators from Paths

Moshe Eliasof, Eldad Haber, Eran Treister; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5878-5891

Discrete Tree Flows via Tree-Structured Permutations

Mai Elkady, Hyung Zin Lim, David I Inouye; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5892-5923

For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria

Scott Emmons, Caspar Oesterheld, Andrew Critch, Vincent Conitzer, Stuart Russell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5924-5943

Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints

Alina Ene, Huy Nguyen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5944-5967

Towards Scaling Difference Target Propagation by Learning Backprop Targets

Maxence M Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, Yoshua Bengio; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5968-5987

Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information

Kawin Ethayarajh, Yejin Choi, Swabha Swayamdipta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5988-6008

Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning

Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C Mozer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6009-6033

Variational Sparse Coding with Learned Thresholding

Kion Fallah, Christopher J Rozell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6034-6058

Training Discrete Deep Generative Models via Gapped Straight-Through Estimator

Ting-Han Fan, Ta-Chung Chi, Alexander I. Rudnicky, Peter J Ramadge; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6059-6073

DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck

Jiameng Fan, Wenchao Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6074-6102

Generalized Data Distribution Iteration

Jiajun Fan, Changnan Xiao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6103-6184

Variational Wasserstein gradient flow

Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6185-6215

Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)

Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6216-6234

Bayesian Continuous-Time Tucker Decomposition

Shikai Fang, Akil Narayan, Robert Kirby, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6235-6245

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6246-6283

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

Sadegh Farhadkhani, Rachid Guerraoui, Lê Nguyên Hoang, Oscar Villemaud; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6284-6323

Investigating Generalization by Controlling Normalized Margin

Alexander R Farhang, Jeremy D Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6324-6336

Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games

Gabriele Farina, Chung-Wei Lee, Haipeng Luo, Christian Kroer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6337-6357

Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis

Oisin Faust, Hamza Fawzi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6358-6372

Matching Structure for Dual Learning

Hao Fei, Shengqiong Wu, Yafeng Ren, Meishan Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6373-6391

Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning

Yingjie Fei, Ruitu Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6392-6417

Private frequency estimation via projective geometry

Vitaly Feldman, Jelani Nelson, Huy Nguyen, Kunal Talwar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6418-6433

An Intriguing Property of Geophysics Inversion

Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6434-6446

Principled Knowledge Extrapolation with GANs

Ruili Feng, Jie Xiao, Kecheng Zheng, Deli Zhao, Jingren Zhou, Qibin Sun, Zheng-Jun Zha; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6447-6464

A Resilient Distributed Boosting Algorithm

Yuval Filmus, Idan Mehalel, Shay Moran; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6465-6473

Model-Value Inconsistency as a Signal for Epistemic Uncertainty

Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram Friesen, Feryal Behbahani, Tom Schaul, Andre Barreto, Simon Osindero; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6474-6498

Coordinated Double Machine Learning

Nitai Fingerhut, Matteo Sesia, Yaniv Romano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6499-6513

Conformal Prediction Sets with Limited False Positives

Adam Fisch, Tal Schuster, Tommi Jaakkola, Dr.Regina Barzilay; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6514-6532

Fast Population-Based Reinforcement Learning on a Single Machine

Arthur Flajolet, Claire Bizon Monroc, Karim Beguir, Thomas Pierrot; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6533-6547

Fast Relative Entropy Coding with A* coding

Gergely Flamich, Stratis Markou, Jose Miguel Hernandez-Lobato; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6548-6577

Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness

Adam Foster, Arpi Vezer, Craig A. Glastonbury, Paidi Creed, Samer Abujudeh, Aaron Sim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6578-6621

Label Ranking through Nonparametric Regression

Dimitris Fotakis, Alkis Kalavasis, Eleni Psaroudaki; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6622-6659

A Neural Tangent Kernel Perspective of GANs

Jean-Yves Franceschi, Emmanuel De Bézenac, Ibrahim Ayed, Mickael Chen, Sylvain Lamprier, Patrick Gallinari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6660-6704

Extracting Latent State Representations with Linear Dynamics from Rich Observations

Abraham Frandsen, Rong Ge, Holden Lee; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6705-6725

SPDY: Accurate Pruning with Speedup Guarantees

Elias Frantar, Dan Alistarh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6726-6743

Revisiting the Effects of Stochasticity for Hamiltonian Samplers

Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6744-6778

Bregman Neural Networks

Jordan Frecon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6779-6792

(Non-)Convergence Results for Predictive Coding Networks

Simon Frieder, Thomas Lukasiewicz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6793-6810

Scaling Structured Inference with Randomization

Yao Fu, John Cunningham, Mirella Lapata; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6811-6828

Greedy when Sure and Conservative when Uncertain about the Opponents

Haobo Fu, Ye Tian, Hongxiang Yu, Weiming Liu, Shuang Wu, Jiechao Xiong, Ying Wen, Kai Li, Junliang Xing, Qiang Fu, Wei Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6829-6848

DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks

Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6849-6862

Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning

Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6863-6877

$p$-Laplacian Based Graph Neural Networks

Guoji Fu, Peilin Zhao, Yatao Bian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6878-6917

Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error

Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6918-6943

Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data

Georgi Ganev, Bristena Oprisanu, Emiliano De Cristofaro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6944-6959

The Complexity of k-Means Clustering when Little is Known

Robert Ganian, Thekla Hamm, Viktoriia Korchemna, Karolina Okrasa, Kirill Simonov; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6960-6987

IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data

Tian Gao, Debarun Bhattacharjya, Elliot Nelson, Miao Liu, Yue Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6988-7001

Loss Function Learning for Domain Generalization by Implicit Gradient

Boyan Gao, Henry Gouk, Yongxin Yang, Timothy Hospedales; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7002-7016

On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum

Hongchang Gao, Junyi Li, Heng Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7017-7035

Deep Reference Priors: What is the best way to pretrain a model?

Yansong Gao, Rahul Ramesh, Pratik Chaudhari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7036-7051

On the Equivalence Between Temporal and Static Equivariant Graph Representations

Jianfei Gao, Bruno Ribeiro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7052-7076

Generalizing Gaussian Smoothing for Random Search

Katelyn Gao, Ozan Sener; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7077-7101

Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems

Yue Gao, Ilia Shumailov, Kassem Fawaz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7102-7121

Lazy Estimation of Variable Importance for Large Neural Networks

Yue Gao, Abby Stevens, Garvesh Raskutti, Rebecca Willett; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7122-7143

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7144-7163

Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems

Lucy L Gao, Jane Ye, Haian Yin, Shangzhi Zeng, Jin Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7164-7182

Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

Xiang Gao, Yuqi Zhang, Yingjie Tian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7183-7207

Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification

Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7208-7222

PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation

Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler Summers, John Lygeros; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7223-7240

The power of first-order smooth optimization for black-box non-smooth problems

Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takac, Pavel Dvurechensky, Bin Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7241-7265

A Functional Information Perspective on Model Interpretation

Itai Gat, Nitay Calderon, Roi Reichart, Tamir Hazan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7266-7278

UniRank: Unimodal Bandit Algorithms for Online Ranking

Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7279-7309

Variational Inference with Locally Enhanced Bounds for Hierarchical Models

Tomas Geffner, Justin Domke; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7310-7323

Inducing Causal Structure for Interpretable Neural Networks

Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah Goodman, Christopher Potts; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7324-7338

Achieving Minimax Rates in Pool-Based Batch Active Learning

Claudio Gentile, Zhilei Wang, Tong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7339-7367

Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning

Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7368-7381

Online Learning for Min Sum Set Cover and Pandora’s Box

Evangelia Gergatsouli, Christos Tzamos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7382-7403

Equivariance versus Augmentation for Spherical Images

Jan Gerken, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7404-7421

A Regret Minimization Approach to Multi-Agent Control

Udaya Ghai, Udari Madhushani, Naomi Leonard, Elad Hazan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7422-7434

Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning

Seyed Kamyar Seyed Ghasemipour, Satoshi Kataoka, Byron David, Daniel Freeman, Shixiang Shane Gu, Igor Mordatch; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7435-7469

Faster Privacy Accounting via Evolving Discretization

Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7470-7483

Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations

Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7484-7512

Offline RL Policies Should Be Trained to be Adaptive

Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7513-7530

Breaking the $\sqrtT$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits

Avishek Ghosh, Abishek Sankararaman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7531-7549

SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation

Giorgio Giannone, Ole Winther; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7550-7569

A Joint Exponential Mechanism For Differentially Private Top-$k$

Jennifer Gillenwater, Matthew Joseph, Andres Munoz, Monica Ribero Diaz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7570-7582

Neuro-Symbolic Hierarchical Rule Induction

Claire Glanois, Zhaohui Jiang, Xuening Feng, Paul Weng, Matthieu Zimmer, Dong Li, Wulong Liu, Jianye Hao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7583-7615

It’s Raw! Audio Generation with State-Space Models

Karan Goel, Albert Gu, Chris Donahue, Christopher Re; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7616-7633

RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression

Yu Gong, Greg Mori, Fred Tung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7634-7649

How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity

Chengyue Gong, Lemeng Wu, Qiang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7650-7664

Partial Label Learning via Label Influence Function

Xiuwen Gong, Dong Yuan, Wei Bao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7665-7678

Secure Distributed Training at Scale

Eduard Gorbunov, Alexander Borzunov, Michael Diskin, Max Ryabinin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7679-7739

Retrieval-Augmented Reinforcement Learning

Anirudh Goyal, Abram Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter C Humphreys, Ksenia Konyushova, Michal Valko, Simon Osindero, Timothy Lillicrap, Nicolas Heess, Charles Blundell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7740-7765

The State of Sparse Training in Deep Reinforcement Learning

Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7766-7792

Causal Inference Through the Structural Causal Marginal Problem

Luigi Gresele, Julius Von Kügelgen, Jonas Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7793-7824

Mirror Learning: A Unifying Framework of Policy Optimisation

Jakub Grudzien, Christian A Schroeder De Witt, Jakob Foerster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7825-7844

Adapting k-means Algorithms for Outliers

Christoph Grunau, Václav Rozhoň; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7845-7886

Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics

Yichen Gu, David T Blaauw, Joshua Welch; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7887-7901

Learning Pseudometric-based Action Representations for Offline Reinforcement Learning

Pengjie Gu, Mengchen Zhao, Chen Chen, Dong Li, Jianye Hao, Bo An; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7902-7918

NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields

Shanyan Guan, Huayu Deng, Yunbo Wang, Xiaokang Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7919-7929

Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning

Jiechao Guan, Zhiwu Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7930-7948

Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity

Lin Guan, Sarath Sreedharan, Subbarao Kambhampati; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7949-7967

Large-Scale Graph Neural Architecture Search

Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, Wenwu Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7968-7981

Identifiability Conditions for Domain Adaptation

Ishaan Gulrajani, Tatsunori Hashimoto; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7982-7997

A Parametric Class of Approximate Gradient Updates for Policy Optimization

Ramki Gummadi, Saurabh Kumar, Junfeng Wen, Dale Schuurmans; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7998-8015

Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes

Hongyi Guo, Qi Cai, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8016-8038

No-Regret Learning in Partially-Informed Auctions

Wenshuo Guo, Michael Jordan, Ellen Vitercik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8039-8055

Bounding Training Data Reconstruction in Private (Deep) Learning

Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8056-8071

Adversarially trained neural representations are already as robust as biological neural representations

Chong Guo, Michael Lee, Guillaume Leclerc, Joel Dapello, Yug Rao, Aleksander Madry, James Dicarlo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8072-8081

Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding

Lan-Zhe Guo, Yu-Feng Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8082-8094

Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage

Alan J.X. Guo, Cong Liang, Qing-Hu Hou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8095-8108

Online Continual Learning through Mutual Information Maximization

Yiduo Guo, Bing Liu, Dongyan Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8109-8126

Fast Provably Robust Decision Trees and Boosting

Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8127-8144

Understanding and Improving Knowledge Graph Embedding for Entity Alignment

Lingbing Guo, Qiang Zhang, Zequn Sun, Mingyang Chen, Wei Hu, Huajun Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8145-8156

NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks

Mustafa B Gurbuz, Constantine Dovrolis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8157-8174

Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

Guy Hacohen, Avihu Dekel, Daphna Weinshall; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8175-8195

You Only Cut Once: Boosting Data Augmentation with a Single Cut

Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin, Ian Reid, Lars Petersson, Hongdong Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8196-8212

Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes

Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8213-8229

G-Mixup: Graph Data Augmentation for Graph Classification

Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8230-8248

Private Streaming SCO in $\ell_p$ geometry with Applications in High Dimensional Online Decision Making

Yuxuan Han, Zhicong Liang, Zhipeng Liang, Yang Wang, Yuan Yao, Jiheng Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8249-8279

Off-Policy Reinforcement Learning with Delayed Rewards

Beining Han, Zhizhou Ren, Zuofan Wu, Yuan Zhou, Jian Peng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8280-8303

Adversarial Attacks on Gaussian Process Bandits

Eric Han, Jonathan Scarlett; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8304-8329

Random Gegenbauer Features for Scalable Kernel Methods

Insu Han, Amir Zandieh, Haim Avron; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8330-8358

Stochastic Reweighted Gradient Descent

Ayoub El Hanchi, David Stephens, Chris Maddison; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8359-8374

Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification

Jun-Yi Hang, Min-Ling Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8375-8386

Temporal Difference Learning for Model Predictive Control

Nicklas A Hansen, Hao Su, Xiaolong Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8387-8406

Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning

Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8407-8426

TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm

Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8427-8445

Contextual Information-Directed Sampling

Botao Hao, Tor Lattimore, Chao Qin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8446-8464

GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing

Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8465-8483

Implicit Regularization with Polynomial Growth in Deep Tensor Factorization

Kais Hariz, Hachem Kadri, Stephane Ayache, Maher Moakher, Thierry Artieres; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8484-8501

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses

Keegan Harris, Dung Daniel T Ngo, Logan Stapleton, Hoda Heidari, Steven Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8502-8522

C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra

Yuka Hashimoto, Zhao Wang, Tomoko Matsui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8523-8534

General-purpose, long-context autoregressive modeling with Perceiver AR

Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, Joao Carreira, Jesse Engel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8535-8558

On Distribution Shift in Learning-based Bug Detectors

Jingxuan He, Luca Beurer-Kellner, Martin Vechev; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8559-8580

GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks

Yixuan He, Quan Gan, David Wipf, Gesine D Reinert, Junchi Yan, Mihai Cucuringu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8581-8612

Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning

Bobby He, Mete Ozay; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8613-8634

Sparse Double Descent: Where Network Pruning Aggravates Overfitting

Zheng He, Zeke Xie, Quanzhi Zhu, Zengchang Qin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8635-8659

A Reduction from Linear Contextual Bandits Lower Bounds to Estimations Lower Bounds

Jiahao He, Jiheng Zhang, Rachel Q. Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8660-8677

HyperPrompt: Prompt-based Task-Conditioning of Transformers

Yun He, Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, Yaguang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H. Chi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8678-8690

Label-Descriptive Patterns and Their Application to Characterizing Classification Errors

Michael A. Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8691-8707

NOMU: Neural Optimization-based Model Uncertainty

Jakob M Heiss, Jakob Weissteiner, Hanna S Wutte, Sven Seuken, Josef Teichmann; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8708-8758

Scaling Out-of-Distribution Detection for Real-World Settings

Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8759-8773

Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers

Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael Mahoney; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8774-8795

Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology

Valentin Hofmann, Janet Pierrehumbert, Hinrich Schütze; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8796-8810

Neural Laplace: Learning diverse classes of differential equations in the Laplace domain

Samuel I Holt, Zhaozhi Qian, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8811-8832

Deep Hierarchy in Bandits

Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8833-8851

DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning

Robert Hönig, Yiren Zhao, Robert Mullins; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8852-8866

Equivariant Diffusion for Molecule Generation in 3D

Emiel Hoogeboom, Vı́ctor Garcia Satorras, Clément Vignac, Max Welling; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8867-8887

Conditional GANs with Auxiliary Discriminative Classifier

Liang Hou, Qi Cao, Huawei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8888-8902

AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

Wenzheng Hou, Qianqian Xu, Zhiyong Yang, Shilong Bao, Yuan He, Qingming Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8903-8925

Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling

Jiri Hron, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8926-8945

Learning inverse folding from millions of predicted structures

Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8946-8970

Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation

Pihe Hu, Yu Chen, Longbo Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8971-9019

Neuron Dependency Graphs: A Causal Abstraction of Neural Networks

Yaojie Hu, Jin Tian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9020-9040

Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL

Siyi Hu, Chuanlong Xie, Xiaodan Liang, Xiaojun Chang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9041-9071

On the Role of Discount Factor in Offline Reinforcement Learning

Hao Hu, Yiqin Yang, Qianchuan Zhao, Chongjie Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9072-9098

Transformer Quality in Linear Time

Weizhe Hua, Zihang Dai, Hanxiao Liu, Quoc Le; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9099-9117

Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9118-9147

Forward Operator Estimation in Generative Models with Kernel Transfer Operators

Zhichun Huang, Rudrasis Chakraborty, Vikas Singh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9148-9172

Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits

Jiatai Huang, Yan Dai, Longbo Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9173-9200

Frustratingly Easy Transferability Estimation

Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, Ying Wei; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9201-9225

Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)

Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, Longbo Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9226-9259

Action-Sufficient State Representation Learning for Control with Structural Constraints

Biwei Huang, Chaochao Lu, Liu Leqi, Jose Miguel Hernandez-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9260-9279

3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design

Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9280-9294

SDQ: Stochastic Differentiable Quantization with Mixed Precision

Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Hu Xianghong, Jeffry Wicaksana, Eric Xing, Kwang-Ting Cheng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9295-9309

Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology

Yan Huang, Ying Sun, Zehan Zhu, Changzhi Yan, Jinming Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9310-9345

Efficient Representation Learning via Adaptive Context Pooling

Chen Huang, Walter Talbott, Navdeep Jaitly, Joshua M Susskind; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9346-9355

On the Learning of Non-Autoregressive Transformers

Fei Huang, Tianhua Tao, Hao Zhou, Lei Li, Minlie Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9356-9376

Going Deeper into Permutation-Sensitive Graph Neural Networks

Zhongyu Huang, Yingheng Wang, Chaozhuo Li, Huiguang He; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9377-9409

Directed Acyclic Transformer for Non-Autoregressive Machine Translation

Fei Huang, Hao Zhou, Yang Liu, Hang Li, Minlie Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9410-9428

Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors

Geert-Jan Huizing, Laura Cantini, Gabriel Peyré; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9429-9443

Robust Kernel Density Estimation with Median-of-Means principle

Pierre Humbert, Batiste Le Bars, Ludovic Minvielle; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9444-9465

A data-driven approach for learning to control computers

Peter C Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, Timothy Lillicrap; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9466-9482

Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization

Samuel Hurault, Arthur Leclaire, Nicolas Papadakis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9483-9505

Inverse Contextual Bandits: Learning How Behavior Evolves over Time

Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9506-9524

Datamodels: Understanding Predictions with Data and Data with Predictions

Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9525-9587

Parsimonious Learning-Augmented Caching

Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9588-9601

Bayesian Optimization for Distributionally Robust Chance-constrained Problem

Yu Inatsu, Shion Takeno, Masayuki Karasuyama, Ichiro Takeuchi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9602-9621

LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation

David Ireland, Giovanni Montana; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9622-9638

The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention

Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9639-9659

A Modern Self-Referential Weight Matrix That Learns to Modify Itself

Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9660-9677

Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness

Shinji Ito; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9678-9694

Modeling Strong and Human-Like Gameplay with KL-Regularized Search

Athul Paul Jacob, David J Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu, Anton Bakhtin, Jacob Andreas, Noam Brown; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9695-9728

A deep convolutional neural network that is invariant to time rescaling

Brandon G Jacques, Zoran Tiganj, Aakash Sarkar, Marc Howard, Per Sederberg; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9729-9738

Input Dependent Sparse Gaussian Processes

Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernandez-Lobato; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9739-9759

Regret Minimization with Performative Feedback

Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9760-9785

Biological Sequence Design with GFlowNets

Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9786-9801

Combining Diverse Feature Priors

Saachi Jain, Dimitris Tsipras, Aleksander Madry; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9802-9832

Training Your Sparse Neural Network Better with Any Mask

Ajay Kumar Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9833-9844

Sequential Covariate Shift Detection Using Classifier Two-Sample Tests

Sooyong Jang, Sangdon Park, Insup Lee, Osbert Bastani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9845-9880

Surrogate Likelihoods for Variational Annealed Importance Sampling

Martin Jankowiak, Du Phan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9881-9901

Planning with Diffusion for Flexible Behavior Synthesis

Michael Janner, Yilun Du, Joshua Tenenbaum, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9902-9915

HyperImpute: Generalized Iterative Imputation with Automatic Model Selection

Daniel Jarrett, Bogdan C Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9916-9937

Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization

Adrian Javaloy, Maryam Meghdadi, Isabel Valera; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9938-9964

Towards understanding how momentum improves generalization in deep learning

Samy Jelassi, Yuanzhi Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9965-10040

MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer

Jeewon Jeon, Woojun Kim, Whiyoung Jung, Youngchul Sung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10041-10052

An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming

Jihwan Jeong, Parth Jaggi, Andrew Butler, Scott Sanner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10053-10067

Agnostic Learnability of Halfspaces via Logistic Loss

Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10068-10103

Improving Policy Optimization with Generalist-Specialist Learning

Zhiwei Jia, Xuanlin Li, Zhan Ling, Shuang Liu, Yiran Wu, Hao Su; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10104-10119

Translatotron 2: High-quality direct speech-to-speech translation with voice preservation

Ye Jia, Michelle Tadmor Ramanovich, Tal Remez, Roi Pomerantz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10120-10134

Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards

Huiwen Jia, Cong Shi, Siqian Shen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10135-10160

The Role of Deconfounding in Meta-learning

Yinjie Jiang, Zhengyu Chen, Kun Kuang, Luotian Yuan, Xinhai Ye, Zhihua Wang, Fei Wu, Ying Wei; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10161-10176

Subspace Learning for Effective Meta-Learning

Weisen Jiang, James Kwok, Yu Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10177-10194

Optimal Algorithms for Stochastic Multi-Level Compositional Optimization

Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10195-10216

Antibody-Antigen Docking and Design via Hierarchical Structure Refinement

Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10217-10227

Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood

Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10228-10250

The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces

Chi Jin, Qinghua Liu, Tiancheng Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10251-10279

Domain Adaptation for Time Series Forecasting via Attention Sharing

Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, Yuyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10280-10297

Accelerated Federated Learning with Decoupled Adaptive Optimization

Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, Dejing Dou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10298-10322

Supervised Off-Policy Ranking

Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, Tie-Yan Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10323-10339

Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing

Jiayin Jin, Zeru Zhang, Yang Zhou, Lingfei Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10340-10361

Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

Jaehyeong Jo, Seul Lee, Sung Ju Hwang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10362-10383

Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits

Marc Jourdan, Rémy Degenne; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10384-10430

Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees

Haotian Ju, Dongyue Li, Hongyang R Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10431-10461

Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation

Justin Jude, Matthew Perich, Lee Miller, Matthias Hennig; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10462-10475

On Measuring Causal Contributions via do-interventions

Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Bloebaum, Elias Bareinboim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10476-10501

Efficient Approximate Inference for Stationary Kernel on Frequency Domain

Yohan Jung, Kyungwoo Song, Jinkyoo Park; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10502-10538

Sketching Algorithms and Lower Bounds for Ridge Regression

Praneeth Kacham, David Woodruff; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10539-10556

Flashlight: Enabling Innovation in Tools for Machine Learning

Jacob D Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10557-10574

Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training

Jan Kaiser, Oliver Stein, Annika Eichler; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10575-10585

Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning

Konstantinos Kalais, Sotirios Chatzis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10586-10597

Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning

Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10598-10632

Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data

Gautam Kamath, Xingtu Liu, Huanyu Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10633-10660

Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning

Hidetaka Kamigaito, Katsuhiko Hayashi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10661-10675

Matching Learned Causal Effects of Neural Networks with Domain Priors

Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian, Amit Sharma; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10676-10696

Deduplicating Training Data Mitigates Privacy Risks in Language Models

Nikhil Kandpal, Eric Wallace, Colin Raffel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10697-10707

Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control

Katie Kang, Paula Gradu, Jason J Choi, Michael Janner, Claire Tomlin, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10708-10733

Forget-free Continual Learning with Winning Subnetworks

Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, Chang D. Yoo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10734-10750

Differentially Private Approximate Quantiles

Haim Kaplan, Shachar Schnapp, Uri Stemmer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10751-10761

Simultaneous Graph Signal Clustering and Graph Learning

Abdullah Karaaslanli, Selin Aviyente; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10762-10772

Composing Partial Differential Equations with Physics-Aware Neural Networks

Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, Martin V. Butz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10773-10801

Meta-Learning Hypothesis Spaces for Sequential Decision-making

Parnian Kassraie, Jonas Rothfuss, Andreas Krause; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10802-10824

FOCUS: Familiar Objects in Common and Uncommon Settings

Priyatham Kattakinda, Soheil Feizi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10825-10847

Training OOD Detectors in their Natural Habitats

Julian Katz-Samuels, Julia B Nakhleh, Robert Nowak, Yixuan Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10848-10865

Robustness Implies Generalization via Data-Dependent Generalization Bounds

Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10866-10894

Generating Distributional Adversarial Examples to Evade Statistical Detectors

Yigitcan Kaya, Muhammad Bilal Zafar, Sergul Aydore, Nathalie Rauschmayr, Krishnaram Kenthapadi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10895-10911

Secure Quantized Training for Deep Learning

Marcel Keller, Ke Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10912-10938

A Convergent and Dimension-Independent Min-Max Optimization Algorithm

Vijay Keswani, Oren Mangoubi, Sushant Sachdeva, Nisheeth K. Vishnoi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10939-10973

Neural Network Poisson Models for Behavioural and Neural Spike Train Data

Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10974-10996

Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling

Sajad Khodadadian, Pranay Sharma, Gauri Joshi, Siva Theja Maguluri; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10997-11057

Multi-Level Branched Regularization for Federated Learning

Jinkyu Kim, Geeho Kim, Bohyung Han; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11058-11073

Learning fair representation with a parametric integral probability metric

Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11074-11101

Dataset Condensation via Efficient Synthetic-Data Parameterization

Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11102-11118

Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance

Heeseung Kim, Sungwon Kim, Sungroh Yoon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11119-11133

Variational On-the-Fly Personalization

Jangho Kim, Jun-Tae Lee, Simyung Chang, Nojun Kwak; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11134-11147

Fisher SAM: Information Geometry and Sharpness Aware Minimisation

Minyoung Kim, Da Li, Shell X Hu, Timothy Hospedales; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11148-11161

ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder

Sangwon Kim, Jaeyeal Nam, Byoung Chul Ko; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11162-11172

Sanity Simulations for Saliency Methods

Joon Sik Kim, Gregory Plumb, Ameet Talwalkar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11173-11200

Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation

Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, Il-Chul Moon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11201-11228

Rotting Infinitely Many-Armed Bandits

Jung-Hun Kim, Milan Vojnovic, Se-Young Yun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11229-11254

Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis

Jungbin Kim, Insoon Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11255-11282

Generalizing to New Physical Systems via Context-Informed Dynamics Model

Matthieu Kirchmeyer, Yuan Yin, Jeremie Dona, Nicolas Baskiotis, Alain Rakotomamonjy, Patrick Gallinari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11283-11301

SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals

Dani Kiyasseh, Tingting Zhu, David A Clifton; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11302-11340

Curriculum Reinforcement Learning via Constrained Optimal Transport

Pascal Klink, Haoyi Yang, Carlo D’Eramo, Jan Peters, Joni Pajarinen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11341-11358

Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups

David M. Knigge, David W Romero, Erik J Bekkers; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11359-11386

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework

Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11387-11412

Transfer Learning In Differential Privacy’s Hybrid-Model

Refael Kohen, Or Sheffet; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11413-11429

Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems

Lukas Köhs, Bastian Alt, Heinz Koeppl; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11430-11454

Partial disentanglement for domain adaptation

Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11455-11472

Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback

Fang Kong, Yichi Zhou, Shuai Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11473-11482

Adaptive Data Analysis with Correlated Observations

Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11483-11498

Controlling Conditional Language Models without Catastrophic Forgetting

Tomasz Korbak, Hady Elsahar, German Kruszewski, Marc Dymetman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11499-11528

Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity

Vladimir R. Kostic, Saverio Salzo, Massimiliano Pontil; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11529-11558

Certified Adversarial Robustness Under the Bounded Support Set

Yiwen Kou, Qinyuan Zheng, Yisen Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11559-11597

Exact Learning of Preference Structure: Single-peaked Preferences and Beyond

Sonja Kraiczy, Edith Elkind; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11598-11612

Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series

Daniel Kramer, Philine L Bommer, Carlo Tombolini, Georgia Koppe, Daniel Durstewitz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11613-11633

Probabilistic ODE Solutions in Millions of Dimensions

Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11634-11649

Active Nearest Neighbor Regression Through Delaunay Refinement

Alexander Kravberg, Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11650-11664

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions

Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11665-11682

Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation

Volodymyr Kuleshov, Shachi Deshpande; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11683-11693

ActiveHedge: Hedge meets Active Learning

Bhuvesh Kumar, Jacob D Abernethy, Venkatesh Saligrama; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11694-11709

Balancing Discriminability and Transferability for Source-Free Domain Adaptation

Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni, Varun Jampani, Venkatesh Babu Radhakrishnan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11710-11728

Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters

Vladislav Kurenkov, Sergey Kolesnikov; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11729-11752

Equivariant Priors for compressed sensing with unknown orientation

Anna Kuzina, Kumar Pratik, Fabio Valerio Massoli, Arash Behboodi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11753-11771

Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11772-11789

Large Batch Experience Replay

Thibault Lahire, Matthieu Geist, Emmanuel Rachelson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11790-11813

FedScale: Benchmarking Model and System Performance of Federated Learning at Scale

Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha Madhyastha, Mosharaf Chowdhury; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11814-11827

Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data

Zhengfeng Lai, Chao Wang, Henrry Gunawan, Sen-Ching S Cheung, Chen-Nee Chuah; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11828-11843

Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses

Alex Lambert, Dimitri Bouche, Zoltan Szabo, Florence D’Alché-Buc; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11844-11867

Tell me why! Explanations support learning relational and causal structure

Andrew K Lampinen, Nicholas Roy, Ishita Dasgupta, Stephanie Cy Chan, Allison Tam, James Mcclelland, Chen Yan, Adam Santoro, Neil C Rabinowitz, Jane Wang, Felix Hill; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11868-11890

Generative Cooperative Networks for Natural Language Generation

Sylvain Lamprier, Thomas Scialom, Antoine Chaffin, Vincent Claveau, Ewa Kijak, Jacopo Staiano, Benjamin Piwowarski; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11891-11905

DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting

Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, Pyang Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11906-11917

Cooperative Online Learning in Stochastic and Adversarial MDPs

Tal Lancewicki, Aviv Rosenberg, Yishay Mansour; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11918-11968

PINs: Progressive Implicit Networks for Multi-Scale Neural Representations

Zoe Landgraf, Alexander Sorkine Hornung, Ricardo S Cabral; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11969-11984

Co-training Improves Prompt-based Learning for Large Language Models

Hunter Lang, Monica N Agrawal, Yoon Kim, David Sontag; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11985-12003

Goal Misgeneralization in Deep Reinforcement Learning

Lauro Langosco Di Langosco, Jack Koch, Lee D Sharkey, Jacob Pfau, David Krueger; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12004-12019

Marginal Tail-Adaptive Normalizing Flows

Mike Laszkiewicz, Johannes Lederer, Asja Fischer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12020-12048

Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes

Tim Tsz-Kit Lau, Han Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12049-12077

Scalable Deep Reinforcement Learning Algorithms for Mean Field Games

Mathieu Lauriere, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Perolat, Romuald Elie, Olivier Pietquin, Matthieu Geist; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12078-12095

Implicit Bias of Linear Equivariant Networks

Hannah Lawrence, Kristian Georgiev, Andrew Dienes, Bobak T. Kiani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12096-12125

Differentially Private Maximal Information Coefficients

John Lazarsfeld, Aaron Johnson, Emmanuel Adeniran; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12126-12163

Entropic Gromov-Wasserstein between Gaussian Distributions

Khang Le, Dung Q Le, Huy Nguyen, Dat Do, Tung Pham, Nhat Ho; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12164-12203

Neurocoder: General-Purpose Computation Using Stored Neural Programs

Hung Le, Svetha Venkatesh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12204-12221

Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime

James-Michael Leahy, Bekzhan Kerimkulov, David Siska, Lukasz Szpruch; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12222-12252

A Random Matrix Analysis of Data Stream Clustering: Coping With Limited Memory Resources

Hugo Lebeau, Romain Couillet, Florent Chatelain; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12253-12281

Neural Tangent Kernel Analysis of Deep Narrow Neural Networks

Jongmin Lee, Joo Young Choi, Ernest K Ryu, Albert No; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12282-12351

Dataset Condensation with Contrastive Signals

Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12352-12364

Confidence Score for Source-Free Unsupervised Domain Adaptation

Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12365-12377

A Statistical Manifold Framework for Point Cloud Data

Yonghyeon Lee, Seungyeon Kim, Jinwon Choi, Frank Park; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12378-12402

Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions

Eunsang Lee, Joon-Woo Lee, Junghyun Lee, Young-Sik Kim, Yongjune Kim, Jong-Seon No, Woosuk Choi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12403-12422

Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert

Yoonhyung Lee, Sungdong Lee, Joong-Ho Won; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12423-12454

Maslow’s Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation

Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath, Sebastian Goldt, Andrew Saxe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12455-12477

Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization

Deokjae Lee, Seungyong Moon, Junhyeok Lee, Hyun Oh Song; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12478-12497

Least Squares Estimation using Sketched Data with Heteroskedastic Errors

Sokbae Lee, Serena Ng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12498-12520

Why the Rich Get Richer? On the Balancedness of Random Partition Models

Changwoo J Lee, Huiyan Sang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12521-12541

Model Selection in Batch Policy Optimization

Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12542-12569

Supervised Learning with General Risk Functionals

Liu Leqi, Audrey Huang, Zachary Lipton, Kamyar Azizzadenesheli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12570-12592

Generalized Strategic Classification and the Case of Aligned Incentives

Sagi Levanon, Nir Rosenfeld; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12593-12618

A Simple Unified Framework for High Dimensional Bandit Problems

Wenjie Li, Adarsh Barik, Jean Honorio; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12619-12655

Robust Training of Neural Networks Using Scale Invariant Architectures

Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank Reddi, Sanjiv Kumar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12656-12684

Spatial-Channel Token Distillation for Vision MLPs

Yanxi Li, Xinghao Chen, Minjing Dong, Yehui Tang, Yunhe Wang, Chang Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12685-12695

An Analytical Update Rule for General Policy Optimization

Hepeng Li, Nicholas Clavette, Haibo He; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12696-12716

On Convergence of Gradient Descent Ascent: A Tight Local Analysis

Haochuan Li, Farzan Farnia, Subhro Das, Ali Jadbabaie; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12717-12740

On the Finite-Time Performance of the Knowledge Gradient Algorithm

Yanwen Li, Siyang Gao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12741-12764

Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning

Yunfei Li, Tian Gao, Jiaqi Yang, Huazhe Xu, Yi Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12765-12781

G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters

Mingjie Li, Xiaojun Guo, Yifei Wang, Yisen Wang, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12782-12796

Decomposing Temporal High-Order Interactions via Latent ODEs

Shibo Li, Robert Kirby, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12797-12812

Neural Inverse Transform Sampler

Henry Li, Yuval Kluger; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12813-12825

PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information

Changbin Li, Suraj Kothawade, Feng Chen, Rishabh Iyer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12826-12842

Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning

Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12843-12856

C-MinHash: Improving Minwise Hashing with Circulant Permutation

Xiaoyun Li, Ping Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12857-12887

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12888-12900

Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the $O(ε^-7/4)$ Complexity

Huan Li, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12901-12916

Achieving Fairness at No Utility Cost via Data Reweighing with Influence

Peizhao Li, Hongfu Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12917-12930

High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails

Shaojie Li, Yong Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12931-12963

MetAug: Contrastive Learning via Meta Feature Augmentation

Jiangmeng Li, Wenwen Qiang, Changwen Zheng, Bing Su, Hui Xiong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12964-12978

PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration

Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12979-12997

CerDEQ: Certifiable Deep Equilibrium Model

Mingjie Li, Yisen Wang, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12998-13013

Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling

Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13014-13051

Let Invariant Rationale Discovery Inspire Graph Contrastive Learning

Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13052-13065

Difference Advantage Estimation for Multi-Agent Policy Gradients

Yueheng Li, Guangming Xie, Zongqing Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13066-13085

Private Adaptive Optimization with Side information

Tian Li, Manzil Zaheer, Sashank Reddi, Virginia Smith; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13086-13105

Permutation Search of Tensor Network Structures via Local Sampling

Chao Li, Junhua Zeng, Zerui Tao, Qibin Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13106-13124

Hessian-Free High-Resolution Nesterov Acceleration For Sampling

Ruilin Li, Hongyuan Zha, Molei Tao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13125-13162

Double Sampling Randomized Smoothing

Linyi Li, Jiawei Zhang, Tao Xie, Bo Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13163-13208

HousE: Knowledge Graph Embedding with Householder Parameterization

Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13209-13224

Learning Multiscale Transformer Models for Sequence Generation

Bei Li, Tong Zheng, Yi Jing, Chengbo Jiao, Tong Xiao, Jingbo Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13225-13241

Finding Global Homophily in Graph Neural Networks When Meeting Heterophily

Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13242-13256

Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows

Feynman Liang, Michael Mahoney, Liam Hodgkinson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13257-13270

Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling

Yuanbang Liang, Jing Wu, Yu-Kun Lai, Yipeng Qin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13271-13284

Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks

Litian Liang, Yaosheng Xu, Stephen Mcaleer, Dailin Hu, Alexander Ihler, Pieter Abbeel, Roy Fox; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13285-13301

TSPipe: Learn from Teacher Faster with Pipelines

Hwijoon Lim, Yechan Kim, Sukmin Yun, Jinwoo Shin, Dongsu Han; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13302-13312

Order Constraints in Optimal Transport

Yu Chin Fabian Lim, Laura Wynter, Shiau Hong Lim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13313-13333

Flow-Guided Sparse Transformer for Video Deblurring

Jing Lin, Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Youliang Yan, Xueyi Zou, Henghui Ding, Yulun Zhang, Radu Timofte, Luc Van Gool; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13334-13343

Federated Learning with Positive and Unlabeled Data

Xinyang Lin, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng, Yunhe Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13344-13355

Decentralized Online Convex Optimization in Networked Systems

Yiheng Lin, Judy Gan, Guannan Qu, Yash Kanoria, Adam Wierman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13356-13393

Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration

Jing Lin, Xiaowan Hu, Yuanhao Cai, Haoqian Wang, Youliang Yan, Xueyi Zou, Yulun Zhang, Luc Van Gool; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13394-13404

Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks

Weiran Lin, Keane Lucas, Lujo Bauer, Michael K. Reiter, Mahmood Sharif; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13405-13430

Learning Augmented Binary Search Trees

Honghao Lin, Tian Luo, David Woodruff; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13431-13440

Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback

Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael Jordan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13441-13467

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments

Jinkun Lin, Anqi Zhang, Mathias Lécuyer, Jinyang Li, Aurojit Panda, Siddhartha Sen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13468-13504

Interactively Learning Preference Constraints in Linear Bandits

David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13505-13527

Delayed Reinforcement Learning by Imitation

Pierre Liotet, Davide Maran, Lorenzo Bisi, Marcello Restelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13528-13556

CITRIS: Causal Identifiability from Temporal Intervened Sequences

Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Stratis Gavves; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13557-13603

StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models

Adam Liska, Tomas Kocisky, Elena Gribovskaya, Tayfun Terzi, Eren Sezener, Devang Agrawal, Cyprien De Masson D’Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan-Mcmahon, Sophia Austin, Phil Blunsom, Angeliki Lazaridou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13604-13622

Distributionally Robust $Q$-Learning

Zijian Liu, Qinxun Bai, Jose Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, Zhengyuan Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13623-13643

Constrained Variational Policy Optimization for Safe Reinforcement Learning

Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Steven Wu, Bo Li, Ding Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13644-13668

Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint

Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13669-13703

Boosting Graph Structure Learning with Dummy Nodes

Xin Liu, Jiayang Cheng, Yangqiu Song, Xin Jiang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13704-13716

Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent

Weiming Liu, Huacong Jiang, Bin Li, Houqiang Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13717-13745

Deep Probability Estimation

Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13746-13781

Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers

Rui Liu, Young Jin Kim, Alexandre Muzio, Hany Hassan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13782-13792

Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games

Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13793-13806

Rethinking Attention-Model Explainability through Faithfulness Violation Test

Yibing Liu, Haoliang Li, Yangyang Guo, Chenqi Kong, Jing Li, Shiqi Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13807-13824

Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training

Risheng Liu, Xuan Liu, Shangzhi Zeng, Jin Zhang, Yixuan Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13825-13856

Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning

Chang Liu, Chenfei Lou, Runzhong Wang, Alan Yuhan Xi, Li Shen, Junchi Yan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13857-13869

Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy

Zhihan Liu, Miao Lu, Zhaoran Wang, Michael Jordan, Zhuoran Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13870-13911

Generating 3D Molecules for Target Protein Binding

Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13912-13924

Communication-efficient Distributed Learning for Large Batch Optimization

Rui Liu, Barzan Mozafari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13925-13946

Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction

Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy Nguyen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13947-13994

REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer

Xingyu Liu, Deepak Pathak, Kris Kitani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13995-14007

Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots

Yuanyuan Liu, Fanhua Shang, Weixin An, Hongying Liu, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14008-14035

Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits

Qinghua Liu, Yuanhao Wang, Chi Jin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14036-14053

Local Augmentation for Graph Neural Networks

Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14054-14072

Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language

Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté, Pierre-Yves Oudeyer, Alexander Schwing; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14073-14093

Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation

Zhihan Liu, Yufeng Zhang, Zuyue Fu, Zhuoran Yang, Zhaoran Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14094-14138

GACT: Activation Compressed Training for Generic Network Architectures

Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, Michael Mahoney, Alvin Cheung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14139-14152

Robust Training under Label Noise by Over-parameterization

Sheng Liu, Zhihui Zhu, Qing Qu, Chong You; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14153-14172

Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization

Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, Jun Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14173-14196

On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games

Robert Loftin, Frans A Oliehoek; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14197-14209

AutoIP: A United Framework to Integrate Physics into Gaussian Processes

Da Long, Zheng Wang, Aditi Krishnapriyan, Robert Kirby, Shandian Zhe, Michael Mahoney; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14210-14222

Bayesian Model Selection, the Marginal Likelihood, and Generalization

Sanae Lotfi, Pavel Izmailov, Gregory Benton, Micah Goldblum, Andrew Gordon Wilson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14223-14247

Feature Learning and Signal Propagation in Deep Neural Networks

Yizhang Lou, Chris E Mingard, Soufiane Hayou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14248-14282

Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension

Bruno Loureiro, Cedric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14283-14314

A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization

Songtao Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14315-14357

Additive Gaussian Processes Revisited

Xiaoyu Lu, Alexis Boukouvalas, James Hensman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14358-14383

ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias

Yupu Lu, Shijie Lin, Guanqi Chen, Jia Pan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14384-14397

Model-Free Opponent Shaping

Christopher Lu, Timon Willi, Christian A Schroeder De Witt, Jakob Foerster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14398-14411

Multi-slots Online Matching with High Entropy

Xingyu Lu, Qintong Wu, Wenliang Zhong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14412-14428

Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching

Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14429-14460

Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering

Ekdeep Lubana, Chi Ian Tang, Fahim Kawsar, Robert Dick, Akhil Mathur; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14461-14484

A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions

Daniel D Lundstrom, Tianjian Huang, Meisam Razaviyayn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14485-14508

BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression

Zhao Tang Luo, Huiyan Sang, Bani Mallick; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14509-14526

Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring

Zhengquan Luo, Yunlong Wang, Zilei Wang, Zhenan Sun, Tieniu Tan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14527-14541

Channel Importance Matters in Few-Shot Image Classification

Xu Luo, Jing Xu, Zenglin Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14542-14559

Learning Dynamics and Generalization in Deep Reinforcement Learning

Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14560-14581

On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis

Qi Lyu, Xiao Fu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14582-14600

Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning

Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14601-14638

Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching

Yecheng Ma, Andrew Shen, Dinesh Jayaraman, Osbert Bastani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14639-14663

Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding

Haotian Ma, Hao Zhang, Fan Zhou, Yinqing Zhang, Quanshi Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14664-14698

Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings

Jan Macdonald, Mathieu E. Besançon, Sebastian Pokutta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14699-14716

A Tighter Analysis of Spectral Clustering, and Beyond

Peter Macgregor, He Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14717-14742

Zero-Shot Reward Specification via Grounded Natural Language

Parsa Mahmoudieh, Deepak Pathak, Trevor Darrell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14743-14752

Feature selection using e-values

Subhabrata Majumdar, Snigdhansu Chatterjee; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14753-14773

Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations

Bodhisattwa Prasad Majumder, Oana Camburu, Thomas Lukasiewicz, Julian Mcauley; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14786-14801

Nonparametric Involutive Markov Chain Monte Carlo

Carol Mak, Fabian Zaiser, Luke Ong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14802-14859

Architecture Agnostic Federated Learning for Neural Networks

Disha Makhija, Xing Han, Nhat Ho, Joydeep Ghosh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14860-14870

Robustness in Multi-Objective Submodular Optimization: a Quantile Approach

Cedric Malherbe, Kevin Scaman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14871-14886

More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees

Osman Asif Malik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14887-14917

Unaligned Supervision for Automatic Music Transcription in The Wild

Ben Maman, Amit H Bermano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14918-14934

Decision-Focused Learning: Through the Lens of Learning to Rank

Jayanta Mandi, Vı́ctor Bucarey, Maxime Mulamba Ke Tchomba, Tias Guns; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14935-14947

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization

Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14948-14978

Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models

Tudor Manole, Nhat Ho; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14979-15006

On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning

Weichao Mao, Lin Yang, Kaiqing Zhang, Tamer Basar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15007-15049

On the Effects of Artificial Data Modification

Antonia Marcu, Adam Prugel-Bennett; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15050-15069

Personalized Federated Learning through Local Memorization

Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15070-15092

Nested Bandits

Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier, Houssam Zenati; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15093-15121

Closed-Form Diffeomorphic Transformations for Time Series Alignment

Iñigo Martinez, Elisabeth Viles, Igor G. Olaizola; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15122-15158

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15159-15179

Modular Conformal Calibration

Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15180-15195

Continual Repeated Annealed Flow Transport Monte Carlo

Alex Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15196-15219

How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation

Augustine Mavor-Parker, Kimberly Young, Caswell Barry, Lewis Griffin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15220-15240

How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection

Mantas Mazeika, Bo Li, David Forsyth; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15241-15254

Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features

Rahul Mazumder, Xiang Meng, Haoyue Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15255-15277

Optimizing Tensor Network Contraction Using Reinforcement Learning

Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15278-15292

Causal Transformer for Estimating Counterfactual Outcomes

Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15293-15329

Steerable 3D Spherical Neurons

Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15330-15339

Transformers are Meta-Reinforcement Learners

Luckeciano C Melo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15340-15359

ButterflyFlow: Building Invertible Layers with Butterfly Matrices

Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15360-15375

In defense of dual-encoders for neural ranking

Aditya Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15376-15400

Equivariant Quantum Graph Circuits

Peter Mernyei, Konstantinos Meichanetzidis, Ismail Ilkan Ceylan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15401-15420

Stochastic Rising Bandits

Alberto Maria Metelli, Francesco Trovò, Matteo Pirola, Marcello Restelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15421-15457

Minimizing Control for Credit Assignment with Strong Feedback

Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera, João Sacramento, Benjamin F. Grewe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15458-15483

A Dynamical System Perspective for Lipschitz Neural Networks

Laurent Meunier, Blaise J Delattre, Alexandre Araujo, Alexandre Allauzen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15484-15500

Distribution Regression with Sliced Wasserstein Kernels

Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15501-15523

Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism

Siqi Miao, Mia Liu, Pan Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15524-15543

Modeling Structure with Undirected Neural Networks

Tsvetomila Mihaylova, Vlad Niculae, Andre Martins; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15544-15560

Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models

Beren Millidge, Tommaso Salvatori, Yuhang Song, Thomas Lukasiewicz, Rafal Bogacz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15561-15583

Learning Stochastic Shortest Path with Linear Function Approximation

Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15584-15629

Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt

Sören Mindermann, Jan M Brauner, Muhammed T Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15630-15649

POEM: Out-of-Distribution Detection with Posterior Sampling

Yifei Ming, Ying Fan, Yixuan Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15650-15665

A Simple Reward-free Approach to Constrained Reinforcement Learning

Sobhan Miryoosefi, Chi Jin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15666-15698

Wide Neural Networks Forget Less Catastrophically

Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu, Dilan Gorur, Mehrdad Farajtabar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15699-15717

Proximal and Federated Random Reshuffling

Konstantin Mishchenko, Ahmed Khaled, Peter Richtarik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15718-15749

ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!

Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, Peter Richtarik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15750-15769

Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions

Aaron Mishkin, Arda Sahiner, Mert Pilanci; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15770-15816

Memory-Based Model Editing at Scale

Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, Chelsea Finn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15817-15831

Invariant Ancestry Search

Phillip B Mogensen, Nikolaj Thams, Jonas Peters; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15832-15857

Differentially Private Community Detection for Stochastic Block Models

Mohamed S Mohamed, Dung Nguyen, Anil Vullikanti, Ravi Tandon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15858-15894

A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity

Michinari Momma, Chaosheng Dong, Jia Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15895-15907

EqR: Equivariant Representations for Data-Efficient Reinforcement Learning

Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15908-15926

Feature and Parameter Selection in Stochastic Linear Bandits

Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori, Mahnoosh Alizadeh, Mohammad Ghavamzadeh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15927-15958

Power-Law Escape Rate of SGD

Takashi Mori, Liu Ziyin, Kangqiao Liu, Masahito Ueda; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15959-15975

Rethinking Fano’s Inequality in Ensemble Learning

Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki, Nobuo Nukaga; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15976-16016

SpeqNets: Sparsity-aware permutation-equivariant graph networks

Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16017-16042

CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer

Yao Mark Mu, Shoufa Chen, Mingyu Ding, Jianyu Chen, Runjian Chen, Ping Luo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16043-16061

Generalized Beliefs for Cooperative AI

Darius Muglich, Luisa M Zintgraf, Christian A Schroeder De Witt, Shimon Whiteson, Jakob Foerster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16062-16082

Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis

Alexander Munteanu, Simon Omlor, Zhao Song, David Woodruff; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16083-16122

Constants Matter: The Performance Gains of Active Learning

Stephen O Mussmann, Sanjoy Dasgupta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16123-16173

On the Generalization Analysis of Adversarial Learning

Waleed Mustafa, Yunwen Lei, Marius Kloft; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16174-16196

Universal and data-adaptive algorithms for model selection in linear contextual bandits

Vidya K Muthukumar, Akshay Krishnamurthy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16197-16222

The Importance of Non-Markovianity in Maximum State Entropy Exploration

Mirco Mutti, Riccardo De Santi, Marcello Restelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16223-16239

PAC-Net: A Model Pruning Approach to Inductive Transfer Learning

Sanghoon Myung, In Huh, Wonik Jang, Jae Myung Choe, Jisu Ryu, Daesin Kim, Kee-Eung Kim, Changwook Jeong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16240-16252

AutoSNN: Towards Energy-Efficient Spiking Neural Networks

Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe, Sungroh Yoon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16253-16269

Implicit Bias of the Step Size in Linear Diagonal Neural Networks

Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro, Daniel Soudry; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16270-16295

DNNR: Differential Nearest Neighbors Regression

Youssef Nader, Leon Sixt, Tim Landgraf; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16296-16317

Overcoming Oscillations in Quantization-Aware Training

Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, Tijmen Blankevoort; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16318-16330

Strategic Representation

Vineet Nair, Ganesh Ghalme, Inbal Talgam-Cohen, Nir Rosenfeld; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16331-16352

Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation

Giung Nam, Hyungi Lee, Byeongho Heo, Juho Lee; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16353-16367

Measuring Representational Robustness of Neural Networks Through Shared Invariances

Vedant Nanda, Till Speicher, Camila Kolling, John P Dickerson, Krishna Gummadi, Adrian Weller; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16368-16382

Tight and Robust Private Mean Estimation with Few Users

Shyam Narayanan, Vahab Mirrokni, Hossein Esfandiari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16383-16412

Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models

Elvis Nava, John Z Zhang, Mike Yan Michelis, Tao Du, Pingchuan Ma, Benjamin F. Grewe, Wojciech Matusik, Robert Kevin Katzschmann; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16413-16427

Multi-Task Learning as a Bargaining Game

Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16428-16446

Variational Inference for Infinitely Deep Neural Networks

Achille Nazaret, David Blei; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16447-16461

Stable Conformal Prediction Sets

Eugene Ndiaye; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16462-16479

Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning

Aviv Netanyahu, Tianmin Shu, Joshua Tenenbaum, Pulkit Agrawal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16480-16495

Sublinear-Time Clustering Oracle for Signed Graphs

Stefan Neumann, Pan Peng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16496-16528

Improved Regret for Differentially Private Exploration in Linear MDP

Dung Daniel T Ngo, Giuseppe Vietri, Steven Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16529-16552

A Framework for Learning to Request Rich and Contextually Useful Information from Humans

Khanh X Nguyen, Yonatan Bisk, Hal Daumé Iii; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16553-16568

Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling

Tung Nguyen, Aditya Grover; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16569-16594

Improving Transformers with Probabilistic Attention Keys

Tam Minh Nguyen, Tan Minh Nguyen, Dung D. D. Le, Duy Khuong Nguyen, Viet-Anh Tran, Richard Baraniuk, Nhat Ho, Stanley Osher; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16595-16621

On Transportation of Mini-batches: A Hierarchical Approach

Khai Nguyen, Dang Nguyen, Quoc Dinh Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16622-16655

Improving Mini-batch Optimal Transport via Partial Transportation

Khai Nguyen, Dang Nguyen, The-Anh Vu-Le, Tung Pham, Nhat Ho; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16656-16690

Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs

Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16691-16723

Optimal Estimation of Policy Gradient via Double Fitted Iteration

Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16724-16783

GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, Mark Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16784-16804

Diffusion Models for Adversarial Purification

Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Animashree Anandkumar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16805-16827

The Primacy Bias in Deep Reinforcement Learning

Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, Aaron Courville; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16828-16847

Causal Conceptions of Fairness and their Consequences

Hamed Nilforoshan, Johann D Gaebler, Ravi Shroff, Sharad Goel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16848-16887

Efficient Test-Time Model Adaptation without Forgetting

Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, Mingkui Tan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16888-16905

Generative Trees: Adversarial and Copycat

Richard Nock, Mathieu Guillame-Bert; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16906-16951

Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules

Juhwan Noh, Dae-Woong Jeong, Kiyoung Kim, Sehui Han, Moontae Lee, Honglak Lee, Yousung Jung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16952-16968

Utilizing Expert Features for Contrastive Learning of Time-Series Representations

Manuel T Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16969-16989

Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval

Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena Hurtado, Aidan N Gomez, Debora Marks, Yarin Gal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16990-17017

Fast Finite Width Neural Tangent Kernel

Roman Novak, Jascha Sohl-Dickstein, Samuel S Schoenholz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17018-17044

Multicoated Supermasks Enhance Hidden Networks

Yasuyuki Okoshi, Ángel López Garcı́a-Arias, Kazutoshi Hirose, Kota Ando, Kazushi Kawamura, Thiem Van Chu, Masato Motomura, Jaehoon Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17045-17055

Generalized Leverage Scores: Geometric Interpretation and Applications

Bruno Ordozgoiti, Antonis Matakos, Aristides Gionis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17056-17070

Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering

Lorenzo Orecchia, Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17071-17093

Anticorrelated Noise Injection for Improved Generalization

Antonio Orvieto, Hans Kersting, Frank Proske, Francis Bach, Aurelien Lucchi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17094-17116

Scalable Deep Gaussian Markov Random Fields for General Graphs

Joel Oskarsson, Per Sidén, Fredrik Lindsten; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17117-17137

Zero-shot AutoML with Pretrained Models

Ekrem Öztürk, Fabio Ferreira, Hadi Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17138-17155

History Compression via Language Models in Reinforcement Learning

Fabian Paischer, Thomas Adler, Vihang Patil, Angela Bitto-Nemling, Markus Holzleitner, Sebastian Lehner, Hamid Eghbal-Zadeh, Sepp Hochreiter; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17156-17185

A Study on the Ramanujan Graph Property of Winning Lottery Tickets

Bithika Pal, Arindam Biswas, Sudeshna Kolay, Pabitra Mitra, Biswajit Basu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17186-17201

On Learning Mixture of Linear Regressions in the Non-Realizable Setting

Soumyabrata Pal, Arya Mazumdar, Rajat Sen, Avishek Ghosh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17202-17220

Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification

Ling Pan, Longbo Huang, Tengyu Ma, Huazhe Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17221-17237

A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks

Yu Pan, Zeyong Su, Ao Liu, Wang Jingquan, Nannan Li, Zenglin Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17238-17257

Robustness and Accuracy Could Be Reconcilable by (Proper) Definition

Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17258-17277

Towards Coherent and Consistent Use of Entities in Narrative Generation

Pinelopi Papalampidi, Kris Cao, Tomas Kocisky; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17278-17294

Constrained Discrete Black-Box Optimization using Mixed-Integer Programming

Theodore P Papalexopoulos, Christian Tjandraatmadja, Ross Anderson, Juan Pablo Vielma, David Belanger; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17295-17322

A Theoretical Comparison of Graph Neural Network Extensions

Pál András Papp, Roger Wattenhofer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17323-17345

Validating Causal Inference Methods

Harsh Parikh, Carlos Varjao, Louise Xu, Eric Tchetgen Tchetgen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17346-17358

The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17359-17371

Learning Symmetric Embeddings for Equivariant World Models

Jung Yeon Park, Ondrej Biza, Linfeng Zhao, Jan-Willem Van De Meent, Robin Walters; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17372-17389

Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness

Namuk Park, Songkuk Kim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17390-17419

Exact Optimal Accelerated Complexity for Fixed-Point Iterations

Jisun Park, Ernest K Ryu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17420-17457

Kernel Methods for Radial Transformed Compositional Data with Many Zeros

Junyoung Park, Changwon Yoon, Cheolwoo Park, Jeongyoun Ahn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17458-17472

Evolving Curricula with Regret-Based Environment Design

Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17473-17498

Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps

Alexandre Pasquiou, Yair Lakretz, John T Hale, Bertrand Thirion, Christophe Pallier; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17499-17516

A new similarity measure for covariate shift with applications to nonparametric regression

Reese Pathak, Cong Ma, Martin Wainwright; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17517-17530

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

Vihang Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M Blies, Johannes Brandstetter, José Arjona-Medina, Sepp Hochreiter; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17531-17572

POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging

Shishir G. Patil, Paras Jain, Prabal Dutta, Ion Stoica, Joseph Gonzalez; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17573-17583

Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning

Max B Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris Maddison; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17584-17600

Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks

Franco Pellegrini, Giulio Biroli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17601-17626

Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding

Yifan Peng, Siddharth Dalmia, Ian Lane, Shinji Watanabe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17627-17643

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17644-17655

Differentiable Top-k Classification Learning

Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17656-17668

Multi-scale Feature Learning Dynamics: Insights for Double Descent

Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17669-17690

A Differential Entropy Estimator for Training Neural Networks

Georg Pichler, Pierre Jean A. Colombo, Malik Boudiaf, Günther Koliander, Pablo Piantanida; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17691-17715

Federated Learning with Partial Model Personalization

Krishna Pillutla, Kshitiz Malik, Abdel-Rahman Mohamed, Mike Rabbat, Maziar Sanjabi, Lin Xiao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17716-17758

Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry

Fabrizio Pittorino, Antonio Ferraro, Gabriele Perugini, Christoph Feinauer, Carlo Baldassi, Riccardo Zecchina; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17759-17781

Geometric Multimodal Contrastive Representation Learning

Petra Poklukar, Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva, Danica Kragic; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17782-17800

Constrained Offline Policy Optimization

Nicholas Polosky, Bruno C. Da Silva, Madalina Fiterau, Jithin Jagannath; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17801-17810

Offline Meta-Reinforcement Learning with Online Self-Supervision

Vitchyr H Pong, Ashvin V Nair, Laura M Smith, Catherine Huang, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17811-17829

Debiaser Beware: Pitfalls of Centering Regularized Transport Maps

Aram-Alexandre Pooladian, Marco Cuturi, Jonathan Niles-Weed; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17830-17847

Adaptive Second Order Coresets for Data-efficient Machine Learning

Omead Pooladzandi, David Davini, Baharan Mirzasoleiman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17848-17869

On the Practicality of Deterministic Epistemic Uncertainty

Janis Postels, Mattia Segù, Tao Sun, Luca Daniel Sieber, Luc Van Gool, Fisher Yu, Federico Tombari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17870-17909

A Simple Guard for Learned Optimizers

Isabeau Prémont-Schwarz, Jaroslav Vı́tků, Jan Feyereisl; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17910-17925

Hardness and Algorithms for Robust and Sparse Optimization

Eric Price, Sandeep Silwal, Samson Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17926-17944

Nonlinear Feature Diffusion on Hypergraphs

Konstantin Prokopchik, Austin R Benson, Francesco Tudisco; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17945-17958

Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows

Michael Puthawala, Matti Lassas, Ivan Dokmanic, Maarten De Hoop; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17959-17983

The Teaching Dimension of Regularized Kernel Learners

Hong Qian, Xu-Hui Liu, Chen-Xi Su, Aimin Zhou, Yang Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17984-18002

ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers

Kaizhi Qian, Yang Zhang, Heting Gao, Junrui Ni, Cheng-I Lai, David Cox, Mark Hasegawa-Johnson, Shiyu Chang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18003-18017

Interventional Contrastive Learning with Meta Semantic Regularizer

Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Bing Su, Hui Xiong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18018-18030

Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost

Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18031-18061

Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder

Tiexin Qin, Shiqi Wang, Haoliang Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18062-18082

Graph Neural Architecture Search Under Distribution Shifts

Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, Wenwu Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18083-18095

Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty

Jixiang Qing, Tom Dhaene, Ivo Couckuyt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18096-18121

Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence

Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18122-18152

Latent Outlier Exposure for Anomaly Detection with Contaminated Data

Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18153-18167

Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning

Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18168-18210

Fast and Provable Nonconvex Tensor RPCA

Haiquan Qiu, Yao Wang, Shaojie Tang, Deyu Meng, Quanming Yao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18211-18249

Generalized Federated Learning via Sharpness Aware Minimization

Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, Zhuo Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18250-18280

Particle Transformer for Jet Tagging

Huilin Qu, Congqiao Li, Sitian Qian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18281-18292

Winning the Lottery Ahead of Time: Efficient Early Network Pruning

John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18293-18309

Convergence of Uncertainty Sampling for Active Learning

Anant Raj, Francis Bach; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18310-18331

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18332-18346

Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization

Alexandre Rame, Corentin Dancette, Matthieu Cord; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18347-18377

A Closer Look at Smoothness in Domain Adversarial Training

Harsh Rangwani, Sumukh K Aithal, Mayank Mishra, Arihant Jain, Venkatesh Babu Radhakrishnan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18378-18399

Linear Adversarial Concept Erasure

Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan D Cotterell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18400-18421

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

Noam Razin, Asaf Maman, Nadav Cohen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18422-18462

One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes

Aravind Reddy, Ryan A. Rossi, Zhao Song, Anup Rao, Tung Mai, Nedim Lipka, Gang Wu, Eunyee Koh, Nesreen Ahmed; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18463-18482

Universality of Winning Tickets: A Renormalization Group Perspective

William T Redman, Tianlong Chen, Zhangyang Wang, Akshunna S. Dogra; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18483-18498

The dynamics of representation learning in shallow, non-linear autoencoders

Maria Refinetti, Sebastian Goldt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18499-18519

Proximal Exploration for Model-guided Protein Sequence Design

Zhizhou Ren, Jiahan Li, Fan Ding, Yuan Zhou, Jianzhu Ma, Jian Peng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18520-18536

Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs

Jie Ren, Mingjie Li, Meng Zhou, Shih-Han Chan, Quanshi Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18537-18558

Benchmarking and Analyzing Point Cloud Classification under Corruptions

Jiawei Ren, Liang Pan, Ziwei Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18559-18575

A Unified View on PAC-Bayes Bounds for Meta-Learning

Arezou Rezazadeh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18576-18595

3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation

Peter Richtarik, Igor Sokolov, Elnur Gasanov, Ilyas Fatkhullin, Zhize Li, Eduard Gorbunov; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18596-18648

Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning

Lorenz Richter, Julius Berner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18649-18666

Probabilistically Robust Learning: Balancing Average and Worst-case Performance

Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18667-18686

LyaNet: A Lyapunov Framework for Training Neural ODEs

Ivan Dario Jimenez Rodriguez, Aaron Ames, Yisong Yue; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18687-18703

Short-Term Plasticity Neurons Learning to Learn and Forget

Hector Garcia Rodriguez, Qinghai Guo, Timoleon Moraitis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18704-18722

Function-space Inference with Sparse Implicit Processes

Simon Rodrı́guez-Santana, Bryan Zaldivar, Daniel Hernandez-Lobato; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18723-18740

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models

Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18741-18753

Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images

Tom Ron, Tamir Hazan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18754-18769

A Consistent and Efficient Evaluation Strategy for Attribution Methods

Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, Enkelejda Kasneci; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18770-18795

Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries

Daniel J Rosenkrantz, Abhijin Adiga, Madhav Marathe, Zirou Qiu, S S Ravi, Richard Stearns, Anil Vullikanti; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18796-18808

Learning to Infer Structures of Network Games

Emanuele Rossi, Federico Monti, Yan Leng, Michael Bronstein, Xiaowen Dong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18809-18827

Direct Behavior Specification via Constrained Reinforcement Learning

Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Chris J Pal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18828-18843

Constraint-based graph network simulator

Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Peter Battaglia; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18844-18870

Continual Learning via Sequential Function-Space Variational Inference

Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18871-18887

Graph-Coupled Oscillator Networks

T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael Bronstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18888-18909

Hindering Adversarial Attacks with Implicit Neural Representations

Andrei A Rusu, Dan Andrei Calian, Sven Gowal, Raia Hadsell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18910-18934

Exploiting Independent Instruments: Identification and Distribution Generalization

Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18935-18958

FedNL: Making Newton-Type Methods Applicable to Federated Learning

Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtarik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18959-19010

Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences

Aadirupa Saha, Pierre Gaillard; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19011-19026

Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits

Aadirupa Saha, Shubham Gupta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19027-19049

Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers

Arda Sahiner, Tolga Ergen, Batu Ozturkler, John Pauly, Morteza Mardani, Mert Pilanci; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19050-19088

Off-Policy Evaluation for Large Action Spaces via Embeddings

Yuta Saito, Thorsten Joachims; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19089-19122

Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training

Charbel Sakr, Steve Dai, Rangha Venkatesan, Brian Zimmer, William Dally, Brucek Khailany; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19123-19138

A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1

Adil Salim, Lukang Sun, Peter Richtarik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19139-19152

FITNESS: (Fine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers

Abishek Sankararaman, Balakrishnan Narayanaswamy, Vikramank Y Singh, Zhao Song; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19153-19177

The Algebraic Path Problem for Graph Metrics

Enrique Fita Sanmartı́n, Sebastian Damrich, Fred Hamprecht; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19178-19204

LSB: Local Self-Balancing MCMC in Discrete Spaces

Emanuele Sansone; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19205-19220

PoF: Post-Training of Feature Extractor for Improving Generalization

Ikuro Sato, Yamada Ryota, Masayuki Tanaka, Nakamasa Inoue, Rei Kawakami; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19221-19230

Re-evaluating Word Mover’s Distance

Ryoma Sato, Makoto Yamada, Hisashi Kashima; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19231-19249

Understanding Contrastive Learning Requires Incorporating Inductive Biases

Nikunj Saunshi, Jordan Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19250-19286

The Neural Race Reduction: Dynamics of Abstraction in Gated Networks

Andrew Saxe, Shagun Sodhani, Sam Jay Lewallen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19287-19309

Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness

Kevin Scaman, Cedric Malherbe, Ludovic Dos Santos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19310-19327

An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings

Meyer Scetbon, Laurent Meunier, Yaniv Romano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19328-19346

Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs

Meyer Scetbon, Gabriel Peyré, Marco Cuturi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19347-19365

Streaming Inference for Infinite Feature Models

Rylan Schaeffer, Yilun Du, Gabrielle K Liu, Ila Fiete; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19366-19387

Modeling Irregular Time Series with Continuous Recurrent Units

Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19388-19405

Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation

Steffen Schotthöfer, Tianbai Xiao, Martin Frank, Cory Hauck; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19406-19433

Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification

Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Bjoern Eskofier, Dario Zanca; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19434-19449

Symmetric Machine Theory of Mind

Melanie Sclar, Graham Neubig, Yonatan Bisk; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19450-19466

Data-SUITE: Data-centric identification of in-distribution incongruous examples

Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19467-19496

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations

Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19497-19521

Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization

Mariia Seleznova, Gitta Kutyniok; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19522-19560

Reinforcement Learning with Action-Free Pre-Training from Videos

Younggyo Seo, Kimin Lee, Stephen L James, Pieter Abbeel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19561-19579

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19580-19597

Selective Regression under Fairness Criteria

Abhin Shah, Yuheng Bu, Joshua K Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W Wornell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19598-19615

Utility Theory for Sequential Decision Making

Mehran Shakerinava, Siamak Ravanbakhsh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19616-19625

Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots

Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19626-19644

A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning

Archit Sharma, Rehaan Ahmad, Chelsea Finn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19645-19657

Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold

Sugandha Sharma, Sarthak Chandra, Ila Fiete; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19658-19682

Federated Minimax Optimization: Improved Convergence Analyses and Algorithms

Pranay Sharma, Rohan Panda, Gauri Joshi, Pramod Varshney; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19683-19730

DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning

Hassam Sheikh, Kizza Frisbee, Mariano Phielipp; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19731-19746

Instance Dependent Regret Analysis of Kernelized Bandits

Shubhanshu Shekhar, Tara Javidi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19747-19772

Data Augmentation as Feature Manipulation

Ruoqi Shen, Sebastien Bubeck, Suriya Gunasekar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19773-19808

Metric-Fair Active Learning

Jie Shen, Nan Cui, Jing Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19809-19826

PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs

Zhengyang Shen, Tao Hong, Qi She, Jinwen Ma, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19827-19846

Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation

Kendrick Shen, Robbie M Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. Haochen, Tengyu Ma, Percy Liang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19847-19878

Constrained Optimization with Dynamic Bound-scaling for Effective NLP Backdoor Defense

Guangyu Shen, Yingqi Liu, Guanhong Tao, Qiuling Xu, Zhuo Zhang, Shengwei An, Shiqing Ma, Xiangyu Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19879-19892

Staged Training for Transformer Language Models

Sheng Shen, Pete Walsh, Kurt Keutzer, Jesse Dodge, Matthew Peters, Iz Beltagy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19893-19908

Deep Network Approximation in Terms of Intrinsic Parameters

Zuowei Shen, Haizhao Yang, Shijun Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19909-19934

Gradient-Free Method for Heavily Constrained Nonconvex Optimization

Wanli Shi, Hongchang Gao, Bin Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19935-19955

Global Optimization of K-Center Clustering

Mingfei Shi, Kaixun Hua, Jiayang Ren, Yankai Cao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19956-19966

Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity

Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19967-20025

Adversarial Masking for Self-Supervised Learning

Yuge Shi, N Siddharth, Philip Torr, Adam R Kosiorek; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20026-20040

Visual Attention Emerges from Recurrent Sparse Reconstruction

Baifeng Shi, Yale Song, Neel Joshi, Trevor Darrell, Xin Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20041-20056

A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes

Chengchun Shi, Masatoshi Uehara, Jiawei Huang, Nan Jiang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20057-20094

Robust Group Synchronization via Quadratic Programming

Yunpeng Shi, Cole M Wyeth, Gilad Lerman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20095-20105

Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets

Tal Shnitzer, Mikhail Yurochkin, Kristjan Greenewald, Justin M Solomon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20106-20124

Scalable Computation of Causal Bounds

Madhumitha Shridharan, Garud Iyengar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20125-20140

Bit Prioritization in Variational Autoencoders via Progressive Coding

Rui Shu, Stefano Ermon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20141-20155

Fair Representation Learning through Implicit Path Alignment

Changjian Shui, Qi Chen, Jiaqi Li, Boyu Wang, Christian Gagné; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20156-20175

Faster Algorithms for Learning Convex Functions

Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly L Geyer, Venkatesh Saligrama, Brian Kulis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20176-20194

Coin Flipping Neural Networks

Yuval Sieradzki, Nitzan Hodos, Gal Yehuda, Assaf Schuster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20195-20214

Reverse Engineering the Neural Tangent Kernel

James Benjamin Simon, Sajant Anand, Mike Deweese; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20215-20231

Demystifying the Adversarial Robustness of Random Transformation Defenses

Chawin Sitawarin, Zachary J Golan-Strieb, David Wagner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20232-20252

Smoothed Adversarial Linear Contextual Bandits with Knapsacks

Vidyashankar Sivakumar, Shiliang Zuo, Arindam Banerjee; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20253-20277

GenLabel: Mixup Relabeling using Generative Models

Jy-Yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, Kangwook Lee; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20278-20313

Communicating via Markov Decision Processes

Samuel Sokota, Christian A Schroeder De Witt, Maximilian Igl, Luisa M Zintgraf, Philip Torr, Martin Strohmeier, Zico Kolter, Shimon Whiteson, Jakob Foerster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20314-20328

The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks

Hadeel Soliman, Lingfei Zhao, Zhipeng Huang, Subhadeep Paul, Kevin S Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20329-20346

Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning

Kyunghwan Son, Junsu Kim, Sungsoo Ahn, Roben D Delos Reyes, Yung Yi, Jinwoo Shin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20347-20368

TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification

Jaeyun Song, Joonhyung Park, Eunho Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20369-20383

A General Recipe for Likelihood-free Bayesian Optimization

Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20384-20404

Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis

Sho Sonoda, Isao Ishikawa, Masahiro Ikeda; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20405-20422

Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation

Aivar Sootla, Alexander I Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David H Mguni, Jun Wang, Haitham Ammar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20423-20443

Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent

Pedro J Soto, Ilia Ilmer, Haibin Guan, Jun Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20444-20458

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20459-20478

3D Infomax improves GNNs for Molecular Property Prediction

Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20479-20502

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20503-20521

Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks

Lukas Struppek, Dominik Hintersdorf, Antonio De Almeida Correira, Antonia Adler, Kristian Kersting; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20522-20545

Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework

Jiahao Su, Wonmin Byeon, Furong Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20546-20579

Divergence-Regularized Multi-Agent Actor-Critic

Kefan Su, Zongqing Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20580-20603

Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems

Miguel Suau, Jinke He, Matthijs T. J. Spaan, Frans Oliehoek; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20604-20624

Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images

Rakshith Subramanyam, Vivek Narayanaswamy, Mark Naufel, Andreas Spanias, Jayaraman J. Thiagarajan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20625-20639

Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems

Jaewook J Suh, Gyumin Roh, Ernest K Ryu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20640-20667

Do Differentiable Simulators Give Better Policy Gradients?

Hyung Ju Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20668-20696

Intriguing Properties of Input-Dependent Randomized Smoothing

Peter Súkenı́k, Aleksei Kuvshinov, Stephan Günnemann; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20697-20743

Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments

Ryan Sullivan, Jordan K Terry, Benjamin Black, John P Dickerson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20744-20776

AGNAS: Attention-Guided Micro and Macro-Architecture Search

Zihao Sun, Yu Hu, Shun Lu, Longxing Yang, Jilin Mei, Yinhe Han, Xiaowei Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20777-20789

Adaptive Random Walk Gradient Descent for Decentralized Optimization

Tao Sun, Dongsheng Li, Bao Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20790-20809

MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection

Zhenhong Sun, Ming Lin, Xiuyu Sun, Zhiyu Tan, Hao Li, Rong Jin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20810-20826

Out-of-Distribution Detection with Deep Nearest Neighbors

Yiyou Sun, Yifei Ming, Xiaojin Zhu, Yixuan Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20827-20840

Black-Box Tuning for Language-Model-as-a-Service

Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, Xipeng Qiu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20841-20855

Correlated Quantization for Distributed Mean Estimation and Optimization

Ananda Theertha Suresh, Ziteng Sun, Jae Ro, Felix Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20856-20876

Causal Imitation Learning under Temporally Correlated Noise

Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20877-20890

Being Properly Improper

Tyler Sypherd, Richard Nock, Lalitha Sankar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20891-20932

Distributionally-Aware Kernelized Bandit Problems for Risk Aversion

Sho Takemori; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20933-20959

Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound

Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20960-20986

SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization

Yuhta Takida, Takashi Shibuya, Weihsiang Liao, Chieh-Hsin Lai, Junki Ohmura, Toshimitsu Uesaka, Naoki Murata, Shusuke Takahashi, Toshiyuki Kumakura, Yuki Mitsufuji; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20987-21012

A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources

Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21013-21036

N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations

Qingyang Tan, Zherong Pan, Breannan Smith, Takaaki Shiratori, Dinesh Manocha; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21037-21049

Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning

Yunhao Tang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21050-21075

Rethinking Graph Neural Networks for Anomaly Detection

Jianheng Tang, Jiajin Li, Ziqi Gao, Jia Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21076-21089

Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm

Huayi Tang, Yong Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21090-21110

Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning

Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, Xiaowen Chu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21111-21132

Cross-Space Active Learning on Graph Convolutional Networks

Yufei Tao, Hao Wu, Shiyuan Deng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21133-21145

FedNest: Federated Bilevel, Minimax, and Compositional Optimization

Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, Samet Oymak; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21146-21179

Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity

Sebastian Shenghong Tay, Chuan Sheng Foo, Urano Daisuke, Richalynn Leong, Bryan Kian Hsiang Low; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21180-21204

LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood

Piotr Tempczyk, Rafał Michaluk, Lukasz Garncarek, Przemysław Spurek, Jacek Tabor, Adam Golinski; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21205-21231

LCANets: Lateral Competition Improves Robustness Against Corruption and Attack

Michael Teti, Garrett Kenyon, Ben Migliori, Juston Moore; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21232-21252

Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees

Darshan Thaker, Paris Giampouras, Rene Vidal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21253-21271

Generalised Policy Improvement with Geometric Policy Composition

Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Remi Munos, Andre Barreto; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21272-21307

Algorithms for the Communication of Samples

Lucas Theis, Noureldin Y Ahmed; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21308-21328

Consistent Polyhedral Surrogates for Top-k Classification and Variants

Anish Thilagar, Rafael Frongillo, Jessica J Finocchiaro, Emma Goodwill; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21329-21359

On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions

Lai Tian, Kaiwen Zhou, Anthony Man-Cho So; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21360-21379

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses

Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Menard; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21380-21431

Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes

Conor Tillinghast, Zheng Wang, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21432-21448

Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm

Malik Tiomoko, Ekkehard Schnoor, Mohamed El Amine Seddik, Igor Colin, Aladin Virmaux; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21449-21477

Extended Unconstrained Features Model for Exploring Deep Neural Collapse

Tom Tirer, Joan Bruna; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21478-21505

Object Permanence Emerges in a Random Walk along Memory

Pavel Tokmakov, Allan Jabri, Jie Li, Adrien Gaidon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21506-21519

Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more

Elad Tolochinksy, Ibrahim Jubran, Dan Feldman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21520-21547

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Umberto M Tomasini, Antonio Sclocchi, Matthieu Wyart; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21548-21583

Quantifying and Learning Linear Symmetry-Based Disentanglement

Loek Tonnaer, Luis Armando Perez Rey, Vlado Menkovski, Mike Holenderski, Jim Portegies; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21584-21608

A Temporal-Difference Approach to Policy Gradient Estimation

Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21609-21632

Simple and near-optimal algorithms for hidden stratification and multi-group learning

Christopher J Tosh, Daniel Hsu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21633-21657

Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization

Brandon Trabucco, Xinyang Geng, Aviral Kumar, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21658-21676

AnyMorph: Learning Transferable Polices By Inferring Agent Morphology

Brandon Trabucco, Mariano Phielipp, Glen Berseth; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21677-21691

Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them

Florian Tramer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21692-21702

Nesterov Accelerated Shuffling Gradient Method for Convex Optimization

Trang H Tran, Katya Scheinberg, Lam M Nguyen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21703-21732

A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation

Chau Tran, Guo Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21733-21750

Tackling covariate shift with node-based Bayesian neural networks

Trung Q Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21751-21775

Fenrir: Physics-Enhanced Regression for Initial Value Problems

Filip Tronarp, Nathanael Bosch, Philipp Hennig; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21776-21794

Interpretable Off-Policy Learning via Hyperbox Search

Daniel Tschernutter, Tobias Hatt, Stefan Feuerriegel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21795-21827

FriendlyCore: Practical Differentially Private Aggregation

Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21828-21863

Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding

Kazuma K Tsuji, Ken’Ichiro Tanaka, Sebastian Pokutta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21864-21883

Prototype Based Classification from Hierarchy to Fairness

Mycal Tucker, Julie A. Shah; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21884-21900

Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures

Nelson Vadori, Rahul Savani, Thomas Spooner, Sumitra Ganesh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21901-21926

Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech

Aditya R Vaidya, Shailee Jain, Alexander Huth; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21927-21944

Path-Gradient Estimators for Continuous Normalizing Flows

Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21945-21959

Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning

Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21960-21983

EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning

Shay Vargaftik, Ran Ben Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben Itzhak, Michael Mitzenmacher; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21984-22014

Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent

Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22015-22059

Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds

Nate Veldt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22060-22083

The CLRS Algorithmic Reasoning Benchmark

Petar Veličković, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, Charles Blundell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22084-22102

Bregman Power k-Means for Clustering Exponential Family Data

Adithya Vellal, Saptarshi Chakraborty, Jason Q Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22103-22119

Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing

Ramji Venkataramanan, Kevin Kögler, Marco Mondelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22120-22144

Bayesian Optimization under Stochastic Delayed Feedback

Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22145-22167

VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis

Tathagat Verma, Abir De, Yateesh Agrawal, Vishwa Vinay, Soumen Chakrabarti; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22168-22183

Calibrated Learning to Defer with One-vs-All Classifiers

Rajeev Verma, Eric Nalisnick; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22184-22202

Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation

Daniel Vial, Advait Parulekar, Sanjay Shakkottai, R Srikant; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22203-22233

On Implicit Bias in Overparameterized Bilevel Optimization

Paul Vicol, Jonathan P Lorraine, Fabian Pedregosa, David Duvenaud, Roger B Grosse; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22234-22259

Multiclass learning with margin: exponential rates with no bias-variance trade-off

Stefano Vigogna, Giacomo Meanti, Ernesto De Vito, Lorenzo Rosasco; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22260-22269

Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning

Adam R Villaflor, Zhe Huang, Swapnil Pande, John M Dolan, Jeff Schneider; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22270-22283

Bayesian Nonparametrics for Offline Skill Discovery

Valentin Villecroze, Harry Braviner, Panteha Naderian, Chris Maddison, Gabriel Loaiza-Ganem; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22284-22299

Hermite Polynomial Features for Private Data Generation

Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mi Jung Park; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22300-22324

What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us?

Tiffany J Vlaar, Jonathan Frankle; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22325-22341

Multirate Training of Neural Networks

Tiffany J Vlaar, Benedict Leimkuhler; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22342-22360

Provably Adversarially Robust Nearest Prototype Classifiers

Václav Voráček, Matthias Hein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22361-22383

First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach

Andrew J Wagenmaker, Yifang Chen, Max Simchowitz, Simon Du, Kevin Jamieson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22384-22429

Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes

Andrew J Wagenmaker, Yifang Chen, Max Simchowitz, Simon Du, Kevin Jamieson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22430-22456

Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four

Stephan Wäldchen, Sebastian Pokutta, Felix Huber; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22457-22474

Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer

Yue Wan, Chang-Yu Hsieh, Ben Liao, Shengyu Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22475-22490

Safe Exploration for Efficient Policy Evaluation and Comparison

Runzhe Wan, Branislav Kveton, Rui Song; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22491-22511

Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning

Lipeng Wan, Zeyang Liu, Xingyu Chen, Xuguang Lan, Nanning Zheng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22512-22535

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods

Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm H Van Seijen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22536-22561

Fast Lossless Neural Compression with Integer-Only Discrete Flows

Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22562-22575

Accelerating Shapley Explanation via Contributive Cooperator Selection

Guanchu Wang, Yu-Neng Chuang, Mengnan Du, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Hu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22576-22590

Denoised MDPs: Learning World Models Better Than the World Itself

Tongzhou Wang, Simon Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22591-22612

Neural Implicit Dictionary Learning via Mixture-of-Expert Training

Peihao Wang, Zhiwen Fan, Tianlong Chen, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22613-22624

Robust Models Are More Interpretable Because Attributions Look Normal

Zifan Wang, Matt Fredrikson, Anupam Datta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22625-22651

Disentangling Disease-related Representation from Obscure for Disease Prediction

Chu-Ran Wang, Fei Gao, Fandong Zhang, Fangwei Zhong, Yizhou Yu, Yizhou Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22652-22664

Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation

Jiali Wang, Wen Huang, Rujun Jiang, Xudong Li, Alex L Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22665-22679

VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix

Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22680-22690

DynaMixer: A Vision MLP Architecture with Dynamic Mixing

Ziyu Wang, Wenhao Jiang, Yiming M Zhu, Li Yuan, Yibing Song, Wei Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22691-22701

Improving Screening Processes via Calibrated Subset Selection

Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22702-22726

The Geometry of Robust Value Functions

Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22727-22751

What Dense Graph Do You Need for Self-Attention?

Yuxin Wang, Chu-Tak Lee, Qipeng Guo, Zhangyue Yin, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22752-22768

Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation

Wenxiao Wang, Alexander J Levine, Soheil Feizi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22769-22783

Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond

Haoxiang Wang, Bo Li, Han Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22784-22801

Communication-Efficient Adaptive Federated Learning

Yujia Wang, Lu Lin, Jinghui Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22802-22838

Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out

Jun-Kun Wang, Chi-Heng Lin, Andre Wibisono, Bin Hu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22839-22864

Robustness Verification for Contrastive Learning

Zekai Wang, Weiwei Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22865-22883

Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering

Peng Wang, Huikang Liu, Anthony Man-Cho So, Laura Balzano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22884-22918

NP-Match: When Neural Processes meet Semi-Supervised Learning

Jianfeng Wang, Thomas Lukasiewicz, Daniela Massiceti, Xiaolin Hu, Vladimir Pavlovic, Alexandros Neophytou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22919-22934

Iterative Double Sketching for Faster Least-Squares Optimization

Rui Wang, Yanyan Ouyang, Wangli Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22935-22963

What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?

Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, Colin Raffel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22964-22984

Improving Task-free Continual Learning by Distributionally Robust Memory Evolution

Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Tiehang Duan, Mingchen Gao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22985-22998

Risk-Averse No-Regret Learning in Online Convex Games

Zifan Wang, Yi Shen, Michael Zavlanos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22999-23017

Provable Domain Generalization via Invariant-Feature Subspace Recovery

Haoxiang Wang, Haozhe Si, Bo Li, Han Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23018-23033

ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training

Hui-Po Wang, Sebastian Stich, Yang He, Mario Fritz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23034-23054

Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search

Qi Wang, Herke Van Hoof; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23055-23077

Approximately Equivariant Networks for Imperfectly Symmetric Dynamics

Rui Wang, Robin Walters, Rose Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23078-23091

Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points

Yi Wang, Zhiren Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23092-23113

Understanding Instance-Level Impact of Fairness Constraints

Jialu Wang, Xin Eric Wang, Yang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23114-23130

Tractable Uncertainty for Structure Learning

Benjie Wang, Matthew R Wicker, Marta Kwiatkowska; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23131-23150

Causal Dynamics Learning for Task-Independent State Abstraction

Zizhao Wang, Xuesu Xiao, Zifan Xu, Yuke Zhu, Peter Stone; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23151-23180

Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms

Xuchuang Wang, Hong Xie, John C. S. Lui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23181-23212

Generative Coarse-Graining of Molecular Conformations

Wujie Wang, Minkai Xu, Chen Cai, Benjamin K Miller, Tess Smidt, Yusu Wang, Jian Tang, Rafael Gomez-Bombarelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23213-23236

Nonparametric Embeddings of Sparse High-Order Interaction Events

Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23237-23253

When Are Linear Stochastic Bandits Attackable?

Huazheng Wang, Haifeng Xu, Hongning Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23254-23273

DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks

Zhuang Wang, Zhaozhuo Xu, Xinyu Wu, Anshumali Shrivastava, T. S. Eugene Ng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23274-23291

Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications

Bokun Wang, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23292-23317

OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, Hongxia Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23318-23340

How Powerful are Spectral Graph Neural Networks

Xiyuan Wang, Muhan Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23341-23362

Thompson Sampling for Robust Transfer in Multi-Task Bandits

Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23363-23416

Individual Reward Assisted Multi-Agent Reinforcement Learning

Li Wang, Yupeng Zhang, Yujing Hu, Weixun Wang, Chongjie Zhang, Yang Gao, Jianye Hao, Tangjie Lv, Changjie Fan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23417-23432

Removing Batch Normalization Boosts Adversarial Training

Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23433-23445

Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition

Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex J Smola, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23446-23458

Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition

Zheng Wang, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23459-23469

Thompson Sampling for (Combinatorial) Pure Exploration

Siwei Wang, Jun Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23470-23483

Policy Gradient Method For Robust Reinforcement Learning

Yue Wang, Shaofeng Zou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23484-23526

Certifying Out-of-Domain Generalization for Blackbox Functions

Maurice G Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23527-23548

More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize

Alexander Wei, Wei Hu, Jacob Steinhardt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23549-23588

To Smooth or Not? When Label Smoothing Meets Noisy Labels

Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23589-23614

Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets

Hongxin Wei, Lue Tao, Renchunzi Xie, Lei Feng, Bo An; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23615-23630

Mitigating Neural Network Overconfidence with Logit Normalization

Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, Yixuan Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23631-23644

Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics

Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Kawahara Yoshinobu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23645-23667

Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

Yuxin Wen, Jonas A. Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23668-23684

BabelTower: Learning to Auto-parallelized Program Translation

Yuanbo Wen, Qi Guo, Qiang Fu, Xiaqing Li, Jianxing Xu, Yanlin Tang, Yongwei Zhao, Xing Hu, Zidong Du, Ling Li, Chao Wang, Xuehai Zhou, Yunji Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23685-23700

Random Forest Density Estimation

Hongwei Wen, Hanyuan Hang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23701-23722

Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming

Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman, Yang Gao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23723-23750

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization

Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John Cunningham, Jacob Gardner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23751-23780

Measure Estimation in the Barycentric Coding Model

Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, James M Murphy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23781-23803

COLA: Consistent Learning with Opponent-Learning Awareness

Timon Willi, Alistair Hp Letcher, Johannes Treutlein, Jakob Foerster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23804-23831

Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning

Harley E Wiltzer, David Meger, Marc G. Bellemare; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23832-23856

Easy Variational Inference for Categorical Models via an Independent Binary Approximation

Michael T Wojnowicz, Shuchin Aeron, Eric L Miller, Michael Hughes; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23857-23896

Continual Learning with Guarantees via Weight Interval Constraints

Maciej Wołczyk, Karol Piczak, Bartosz Wójcik, Lukasz Pustelnik, Paweł Morawiecki, Jacek Tabor, Tomasz Trzcinski, Przemysław Spurek; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23897-23911

A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications

Lukas Wolf, Ard Kastrati, Martyna B Plomecka, Jie-Ming Li, Dustin Klebe, Alexander Veicht, Roger Wattenhofer, Nicolas Langer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23912-23932

Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time

David Woodruff, Amir Zandieh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23933-23964

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23965-23998

Metric-Fair Classifier Derandomization

Jimmy Wu, Yatong Chen, Yang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23999-24016

Structural Entropy Guided Graph Hierarchical Pooling

Junran Wu, Xueyuan Chen, Ke Xu, Shangzhe Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24017-24030

Self-supervised Models are Good Teaching Assistants for Vision Transformers

Haiyan Wu, Yuting Gao, Yinqi Zhang, Shaohui Lin, Yuan Xie, Xing Sun, Ke Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24031-24042

Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks

Nan Wu, Stanislaw Jastrzebski, Kyunghyun Cho, Krzysztof J Geras; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24043-24055

Instrumental Variable Regression with Confounder Balancing

Anpeng Wu, Kun Kuang, Bo Li, Fei Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24056-24075

MemSR: Training Memory-efficient Lightweight Model for Image Super-Resolution

Kailu Wu, Chung-Kuei Lee, Kaisheng Ma; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24076-24092

Delay-Adaptive Step-sizes for Asynchronous Learning

Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian, Mikael Johansson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24093-24113

Variational nearest neighbor Gaussian process

Luhuan Wu, Geoff Pleiss, John P Cunningham; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24114-24130

Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach

Shuang Wu, Ling Shi, Jun Wang, Guangjian Tian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24131-24149

DAVINZ: Data Valuation using Deep Neural Networks at Initialization

Zhaoxuan Wu, Yao Shu, Bryan Kian Hsiang Low; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24150-24176

Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum

Junlin Wu, Yevgeniy Vorobeychik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24177-24211

Revisiting Consistency Regularization for Deep Partial Label Learning

Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24212-24225

Flowformer: Linearizing Transformers with Conservation Flows

Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24226-24242

Nearly Optimal Policy Optimization with Stable at Any Time Guarantee

Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon Du, Jiantao Jiao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24243-24265

RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval

Yihan Wu, Hongyang Zhang, Heng Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24266-24279

Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression

Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham Kakade; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24280-24314

Optimal Clustering with Noisy Queries via Multi-Armed Bandit

Jinghui Xia, Zengfeng Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24315-24331

ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning

Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z. Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24332-24346

Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm

Lechao Xiao, Jeffrey Pennington; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24347-24369

Identification of Linear Non-Gaussian Latent Hierarchical Structure

Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24370-24387

COAT: Measuring Object Compositionality in Emergent Representations

Sirui Xie, Ari S Morcos, Song-Chun Zhu, Ramakrishna Vedantam; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24388-24413

Robust Policy Learning over Multiple Uncertainty Sets

Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24414-24429

Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum

Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24430-24459

Self-Supervised Representation Learning via Latent Graph Prediction

Yaochen Xie, Zhao Xu, Shuiwang Ji; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24460-24477

Efficient Computation of Higher-Order Subgraph Attribution via Message Passing

Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24478-24495

A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games

Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24496-24523

Importance Weighted Kernel Bayes’ Rule

Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24524-24538

Learning to Separate Voices by Spatial Regions

Alan Xu, Romit Roy Choudhury; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24539-24549

Detached Error Feedback for Distributed SGD with Random Sparsification

An Xu, Heng Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24550-24575

Accurate Quantization of Measures via Interacting Particle-based Optimization

Lantian Xu, Anna Korba, Dejan Slepcev; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24576-24595

Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces

Yinshuang Xu, Jiahui Lei, Edgar Dobriban, Kostas Daniilidis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24596-24614

Inferring Cause and Effect in the Presence of Heteroscedastic Noise

Sascha Xu, Osman A Mian, Alexander Marx, Jilles Vreeken; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24615-24630

Prompting Decision Transformer for Few-Shot Policy Generalization

Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua Tenenbaum, Chuang Gan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24631-24645

Analyzing and Mitigating Interference in Neural Architecture Search

Jin Xu, Xu Tan, Kaitao Song, Renqian Luo, Yichong Leng, Tao Qin, Tie-Yan Liu, Jian Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24646-24662

On the Statistical Benefits of Curriculum Learning

Ziping Xu, Ambuj Tewari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24663-24682

A Difference Standardization Method for Mutual Transfer Learning

Haoqing Xu, Meng Wang, Beilun Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24683-24697

SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks

Xiang Xu, Karl D.D. Willis, Joseph G Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman, Yasutaka Furukawa; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24698-24724

Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations

Haoran Xu, Xianyuan Zhan, Honglei Yin, Huiling Qin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24725-24742

Adversarial Attack and Defense for Non-Parametric Two-Sample Tests

Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24743-24769

Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization

Xiaojun Xu, Jacky Y Zhang, Evelyn Ma, Hyun Ho Son, Sanmi Koyejo, Bo Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24770-24802

A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization

Renzhe Xu, Xingxuan Zhang, Zheyan Shen, Tong Zhang, Peng Cui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24803-24829

Langevin Monte Carlo for Contextual Bandits

Pan Xu, Hongkai Zheng, Eric V Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24830-24850

Investigating Why Contrastive Learning Benefits Robustness against Label Noise

Yihao Xue, Kyle Whitecross, Baharan Mirzasoleiman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24851-24871

Diversified Adversarial Attacks based on Conjugate Gradient Method

Keiichiro Yamamura, Haruki Sato, Nariaki Tateiwa, Nozomi Hata, Toru Mitsutake, Issa Oe, Hiroki Ishikura, Katsuki Fujisawa; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24872-24894

Cycle Representation Learning for Inductive Relation Prediction

Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24895-24910

Optimally Controllable Perceptual Lossy Compression

Zeyu Yan, Fei Wen, Peilin Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24911-24928

Active fairness auditing

Tom Yan, Chicheng Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24929-24962

Self-Organized Polynomial-Time Coordination Graphs

Qianlan Yang, Weijun Dong, Zhizhou Ren, Jianhao Wang, Tonghan Wang, Chongjie Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24963-24979

Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning

Shentao Yang, Yihao Feng, Shujian Zhang, Mingyuan Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24980-25006

A Psychological Theory of Explainability

Scott Cheng-Hsin Yang, Nils Erik Tomas Folke, Patrick Shafto; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25007-25021

Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning

Ling Yang, Shenda Hong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25022-25037

Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion

Ling Yang, Shenda Hong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25038-25054

Searching for BurgerFormer with Micro-Meso-Macro Space Design

Longxing Yang, Yu Hu, Shun Lu, Zihao Sun, Jilin Mei, Yinhe Han, Xiaowei Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25055-25069

Efficient Variance Reduction for Meta-learning

Hansi Yang, James Kwok; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25070-25095

Injecting Logical Constraints into Neural Networks via Straight-Through Estimators

Zhun Yang, Joohyung Lee, Chiyoun Park; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25096-25122

Locally Sparse Neural Networks for Tabular Biomedical Data

Junchen Yang, Ofir Lindenbaum, Yuval Kluger; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25123-25153

Not All Poisons are Created Equal: Robust Training against Data Poisoning

Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25154-25165

Does the Data Induce Capacity Control in Deep Learning?

Rubing Yang, Jialin Mao, Pratik Chaudhari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25166-25197

Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity

Jianyi Yang, Shaolei Ren; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25198-25240

Linear Bandit Algorithms with Sublinear Time Complexity

Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25241-25260

A New Perspective on the Effects of Spectrum in Graph Neural Networks

Mingqi Yang, Yanming Shen, Rui Li, Heng Qi, Qiang Zhang, Baocai Yin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25261-25279

Fourier Learning with Cyclical Data

Yingxiang Yang, Zhihan Xiong, Tianyi Liu, Taiqing Wang, Chong Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25280-25301

Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network

Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25302-25312

A Study of Face Obfuscation in ImageNet

Kaiyu Yang, Jacqueline H. Yau, Li Fei-Fei, Jia Deng, Olga Russakovsky; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25313-25330

Anarchic Federated Learning

Haibo Yang, Xin Zhang, Prashant Khanduri, Jia Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25331-25363

Identity-Disentangled Adversarial Augmentation for Self-supervised Learning

Kaiwen Yang, Tianyi Zhou, Xinmei Tian, Dacheng Tao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25364-25381

Learning from a Learning User for Optimal Recommendations

Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25382-25406

Improving Out-of-Distribution Robustness via Selective Augmentation

Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25407-25437

NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework

Xingcheng Yao, Yanan Zheng, Xiaocong Yang, Zhilin Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25438-25451

Feature Space Particle Inference for Neural Network Ensembles

Shingo Yashima, Teppei Suzuki, Kohta Ishikawa, Ikuro Sato, Rei Kawakami; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25452-25468

Centroid Approximation for Bootstrap: Improving Particle Quality at Inference

Mao Ye, Qiang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25469-25489

Be Like Water: Adaptive Floating Point for Machine Learning

Thomas Yeh, Max Sterner, Zerlina Lai, Brandon Chuang, Alexander Ihler; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25490-25500

QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning

Liping Yi, Wang Gang, Liu Xiaoguang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25501-25513

De novo mass spectrometry peptide sequencing with a transformer model

Melih Yilmaz, William Fondrie, Wout Bittremieux, Sewoong Oh, William S Noble; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25514-25522

Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations

Fan Yin, Jieying Jiao, Jun Yan, Guanyu Hu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25523-25551

Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization

Jaehong Yoon, Geon Park, Wonyong Jeong, Sung Ju Hwang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25552-25565

ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks

Haoran You, Baopu Li, Shi Huihong, Yonggan Fu, Yingyan Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25566-25580

Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks

Zhaoning Yu, Hongyang Gao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25581-25594

Understanding Robust Overfitting of Adversarial Training and Beyond

Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25595-25610

How to Leverage Unlabeled Data in Offline Reinforcement Learning

Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25611-25635

Reachability Constrained Reinforcement Learning

Dongjie Yu, Haitong Ma, Shengbo Li, Jianyu Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25636-25655

Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning

Sixing Yu, Arya Mazaheri, Ali Jannesari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25656-25667

The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks

Xin Yu, Thiago Serra, Srikumar Ramalingam, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25668-25683

GraphFM: Improving Large-Scale GNN Training via Feature Momentum

Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25684-25701

Latent Diffusion Energy-Based Model for Interpretable Text Modelling

Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25702-25720

Predicting Out-of-Distribution Error with the Projection Norm

Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25721-25746

Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning

Haoqi Yuan, Zongqing Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25747-25759

Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance

Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25760-25782

Neural Tangent Kernel Empowered Federated Learning

Kai Yue, Richeng Jin, Ryan Pilgrim, Chau-Wai Wong, Dror Baron, Huaiyu Dai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25783-25803

Time Is MattEr: Temporal Self-supervision for Video Transformers

Sukmin Yun, Jaehyung Kim, Dongyoon Han, Hwanjun Song, Jung-Woo Ha, Jinwoo Shin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25804-25816

Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images

Shiran Zada, Itay Benou, Michal Irani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25817-25833

Adaptive Conformal Predictions for Time Series

Margaux Zaffran, Olivier Feron, Yannig Goude, Julie Josse, Aymeric Dieuleveut; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25834-25866

Actor-Critic based Improper Reinforcement Learning

Mohammadi Zaki, Avi Mohan, Aditya Gopalan, Shie Mannor; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25867-25919

Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning

Andrea Zanette, Martin Wainwright; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25920-25954

Multi Resolution Analysis (MRA) for Approximate Self-Attention

Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, Vikas Singh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25955-25972

Efficient PAC Learning from the Crowd with Pairwise Comparisons

Shiwei Zeng, Jie Shen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25973-25993

Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts

Yan Zeng, Xinsong Zhang, Hang Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25994-26009

Position Prediction as an Effective Pretraining Strategy

Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram, Dan Busbridge, Tatiana Likhomanenko, Joseph Y Cheng, Walter Talbott, Chen Huang, Hanlin Goh, Joshua M Susskind; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26010-26027

Anytime Information Cascade Popularity Prediction via Self-Exciting Processes

Xi Zhang, Akshay Aravamudan, Georgios C Anagnostopoulos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26028-26047

Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy

Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Steven Wu, Jinfeng Yi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26048-26067

Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs

Yikang Zhang, Zhuo Chen, Zhao Zhong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26068-26084

PDE-Based Optimal Strategy for Unconstrained Online Learning

Zhiyu Zhang, Ashok Cutkosky, Ioannis Paschalidis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26085-26115

Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function

Qixin Zhang, Zengde Deng, Zaiyi Chen, Haoyuan Hu, Yu Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26116-26134

When and How Mixup Improves Calibration

Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26135-26160

UAST: Uncertainty-Aware Siamese Tracking

Dawei Zhang, Yanwei Fu, Zhonglong Zheng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26161-26175

Examining Scaling and Transfer of Language Model Architectures for Machine Translation

Biao Zhang, Behrooz Ghorbani, Ankur Bapna, Yong Cheng, Xavier Garcia, Jonathan Shen, Orhan Firat; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26176-26192

Revisiting End-to-End Speech-to-Text Translation From Scratch

Biao Zhang, Barry Haddow, Rico Sennrich; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26193-26205

A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms

Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Nicola Elia; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26206-26222

GALAXY: Graph-based Active Learning at the Extreme

Jifan Zhang, Julian Katz-Samuels, Robert Nowak; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26223-26238

Fairness Interventions as (Dis)Incentives for Strategic Manipulation

Xueru Zhang, Mohammad Mahdi Khalili, Kun Jin, Parinaz Naghizadeh, Mingyan Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26239-26264

Role-based Multiplex Network Embedding

Hegui Zhang, Gang Kou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26265-26280

Dynamic Topic Models for Temporal Document Networks

Delvin Ce Zhang, Hady Lauw; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26281-26292

Personalized Federated Learning via Variational Bayesian Inference

Xu Zhang, Yinchuan Li, Wenpeng Li, Kaiyang Guo, Yunfeng Shao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26293-26310

Federated Learning with Label Distribution Skew via Logits Calibration

Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Chao Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26311-26329

Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective

Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26330-26346

Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity

Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26347-26361

Deep and Flexible Graph Neural Architecture Search

Wentao Zhang, Zheyu Lin, Yu Shen, Yang Li, Zhi Yang, Bin Cui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26362-26374

A Langevin-like Sampler for Discrete Distributions

Ruqi Zhang, Xingchao Liu, Qiang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26375-26396

Rich Feature Construction for the Optimization-Generalization Dilemma

Jianyu Zhang, David Lopez-Paz, Leon Bottou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26397-26411

Generative Flow Networks for Discrete Probabilistic Modeling

Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26412-26428

Neurotoxin: Durable Backdoors in Federated Learning

Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael Mahoney, Prateek Mittal, Ramchandran Kannan, Joseph Gonzalez; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26429-26446

Making Linear MDPs Practical via Contrastive Representation Learning

Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, Bo Dai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26447-26466

NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning

Wentao Zhang, Zeang Sheng, Mingyu Yang, Yang Li, Yu Shen, Zhi Yang, Bin Cui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26467-26483

Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations

Michael Zhang, Nimit S Sohoni, Hongyang R Zhang, Chelsea Finn, Christopher Re; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26484-26516

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach

Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26517-26547

Partial Counterfactual Identification from Observational and Experimental Data

Junzhe Zhang, Jin Tian, Elias Bareinboim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26548-26558

Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets

Lily Zhang, Veronica Tozzo, John Higgins, Rajesh Ranganath; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26559-26574

Learning to Estimate and Refine Fluid Motion with Physical Dynamics

Mingrui Zhang, Jianhong Wang, James B Tlhomole, Matthew Piggott; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26575-26590

A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks

Huan Zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, Zico Kolter; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26591-26604

A Simple yet Universal Strategy for Online Convex Optimization

Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26605-26623

Low-Precision Stochastic Gradient Langevin Dynamics

Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26624-26644

Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control

Liang Zhang, Qiang Wu, Jun Shen, Linyuan Lü, Bo Du, Jianqing Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26645-26654

Uncertainty Modeling in Generative Compressed Sensing

Yilang Zhang, Mengchu Xu, Xiaojun Mao, Jian Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26655-26668

Building Robust Ensembles via Margin Boosting

Dinghuai Zhang, Hongyang Zhang, Aaron Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26669-26692

Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization

Yihua Zhang, Guanhua Zhang, Prashant Khanduri, Mingyi Hong, Shiyu Chang, Sijia Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26693-26712

Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory

Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, Mengdi Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26713-26749

ROCK: Causal Inference Principles for Reasoning about Commonsense Causality

Jiayao Zhang, Hongming Zhang, Weijie Su, Dan Roth; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26750-26771

No-Regret Learning in Time-Varying Zero-Sum Games

Mengxiao Zhang, Peng Zhao, Haipeng Luo, Zhi-Hua Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26772-26808

PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance

Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26809-26823

NysADMM: faster composite convex optimization via low-rank approximation

Shipu Zhao, Zachary Frangella, Madeleine Udell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26824-26840

Toward Compositional Generalization in Object-Oriented World Modeling

Linfeng Zhao, Lingzhi Kong, Robin Walters, Lawson L.S. Wong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26841-26864

Dynamic Regret of Online Markov Decision Processes

Peng Zhao, Long-Fei Li, Zhi-Hua Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26865-26894

Learning to Solve PDE-constrained Inverse Problems with Graph Networks

Qingqing Zhao, David B Lindell, Gordon Wetzstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26895-26910

Learning from Counterfactual Links for Link Prediction

Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, Meng Jiang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26911-26926

Global Optimization Networks

Sen Zhao, Erez Louidor, Maya Gupta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26927-26957

Certified Robustness Against Natural Language Attacks by Causal Intervention

Haiteng Zhao, Chang Ma, Xinshuai Dong, Anh Tuan Luu, Zhi-Hong Deng, Hanwang Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26958-26970

Efficient Learning for AlphaZero via Path Consistency

Dengwei Zhao, Shikui Tu, Lei Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26971-26981

Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning

Yang Zhao, Hao Zhang, Xiuyuan Hu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26982-26992

Ripple Attention for Visual Perception with Sub-quadratic Complexity

Lin Zheng, Huijie Pan, Lingpeng Kong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26993-27010

Linear Complexity Randomized Self-attention Mechanism

Lin Zheng, Chong Wang, Lingpeng Kong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27011-27041

Online Decision Transformer

Qinqing Zheng, Amy Zhang, Aditya Grover; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27042-27059

Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks

Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27060-27074

HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning

Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27075-27098

Describing Differences between Text Distributions with Natural Language

Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27099-27116

Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets

Han Zhong, Wei Xiong, Jiyuan Tan, Liwei Wang, Tong Zhang, Zhaoran Wang, Zhuoran Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27117-27142

Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization

Dongruo Zhou, Quanquan Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27143-27158

A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines

Weichao Zhou, Wenchao Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27159-27178

On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features

Jinxin Zhou, Xiao Li, Tianyu Ding, Chong You, Qing Qu, Zhihui Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27179-27202

Model Agnostic Sample Reweighting for Out-of-Distribution Learning

Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27203-27221

Sparse Invariant Risk Minimization

Xiao Zhou, Yong Lin, Weizhong Zhang, Tong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27222-27244

Prototype-Anchored Learning for Learning with Imperfect Annotations

Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27245-27267

FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27268-27286

Probabilistic Bilevel Coreset Selection

Xiao Zhou, Renjie Pi, Weizhong Zhang, Yong Lin, Zonghao Chen, Tong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27287-27302

Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets

Baojian Zhou, Yifan Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27303-27337

Improving Adversarial Robustness via Mutual Information Estimation

Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27338-27352

Modeling Adversarial Noise for Adversarial Training

Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27353-27366

Contrastive Learning with Boosted Memorization

Zhihan Zhou, Jiangchao Yao, Yan-Feng Wang, Bo Han, Ya Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27367-27377

Understanding The Robustness in Vision Transformers

Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Animashree Anandkumar, Jiashi Feng, Jose M. Alvarez; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27378-27394

VLUE: A Multi-Task Multi-Dimension Benchmark for Evaluating Vision-Language Pre-training

Wangchunshu Zhou, Yan Zeng, Shizhe Diao, Xinsong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27395-27411

Detecting Corrupted Labels Without Training a Model to Predict

Zhaowei Zhu, Zihao Dong, Yang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27412-27427

Contextual Bandits with Large Action Spaces: Made Practical

Yinglun Zhu, Dylan J Foster, John Langford, Paul Mineiro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27428-27453

Neural-Symbolic Models for Logical Queries on Knowledge Graphs

Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, Jian Tang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27454-27478

Topology-aware Generalization of Decentralized SGD

Tongtian Zhu, Fengxiang He, Lan Zhang, Zhengyang Niu, Mingli Song, Dacheng Tao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27479-27503

Resilient and Communication Efficient Learning for Heterogeneous Federated Systems

Zhuangdi Zhu, Junyuan Hong, Steve Drew, Jiayu Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27504-27526

On Numerical Integration in Neural Ordinary Differential Equations

Aiqing Zhu, Pengzhan Jin, Beibei Zhu, Yifa Tang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27527-27547

When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee

Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27548-27573

Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces

Yinglun Zhu, Paul Mineiro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27574-27590

Residual-Based Sampling for Online Outlier-Robust PCA

Tianhao Zhu, Jie Shen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27591-27611

Region-Based Semantic Factorization in GANs

Jiapeng Zhu, Yujun Shen, Yinghao Xu, Deli Zhao, Qifeng Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27612-27632

Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features

Zhaowei Zhu, Jialu Wang, Yang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27633-27653

Towards Uniformly Superhuman Autonomy via Subdominance Minimization

Brian Ziebart, Sanjiban Choudhury, Xinyan Yan, Paul Vernaza; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27654-27670

Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm

Pini Zilber, Boaz Nadler; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27671-27692

Counterfactual Prediction for Outcome-Oriented Treatments

Hao Zou, Bo Li, Jiangang Han, Shuiping Chen, Xuetao Ding, Peng Cui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27693-27706

SpaceMAP: Visualizing High-Dimensional Data by Space Expansion

Xinrui Zu, Qian Tao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27707-27723

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