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Volume 119: International Conference on Machine Learning, 13-18 July 2020, Virtual

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Editors: Hal Daumé III, Aarti Singh

[bib][citeproc]

Selective Dyna-Style Planning Under Limited Model Capacity

Zaheer Abbas, Samuel Sokota, Erin Talvitie, Martha White; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1-10

A distributional view on multi-objective policy optimization

Abbas Abdolmaleki, Sandy Huang, Leonard Hasenclever, Michael Neunert, Francis Song, Martina Zambelli, Murilo Martins, Nicolas Heess, Raia Hadsell, Martin Riedmiller; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11-22

Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation

Marc Abeille, Alessandro Lazaric; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:23-31

Super-efficiency of automatic differentiation for functions defined as a minimum

Pierre Ablin, Gabriel Peyré, Thomas Moreau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:32-41

A Geometric Approach to Archetypal Analysis via Sparse Projections

Vinayak Abrol, Pulkit Sharma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:42-51

Context Aware Local Differential Privacy

Jayadev Acharya, Kallista Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:52-62

Efficient Intervention Design for Causal Discovery with Latents

Raghavendra Addanki, Shiva Kasiviswanathan, Andrew Mcgregor, Cameron Musco; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:63-73

The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization

Ben Adlam, Jeffrey Pennington; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:74-84

Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions

Arpit Agarwal, Shivani Agarwal, Sanjeev Khanna, Prathamesh Patil; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:85-95

Boosting for Control of Dynamical Systems

Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:96-103

An Optimistic Perspective on Offline Reinforcement Learning

Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:104-114

Optimal Bounds between f-Divergences and Integral Probability Metrics

Rohit Agrawal, Thibaut Horel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:115-124

LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments

Ali Ahmaditeshnizi, Saber Salehkaleybar, Negar Kiyavash; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:125-133

Learning What to Defer for Maximum Independent Sets

Sungsoo Ahn, Younggyo Seo, Jinwoo Shin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:134-144

Invariant Risk Minimization Games

Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, Amit Dhurandhar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:145-155

Why bigger is not always better: on finite and infinite neural networks

Laurence Aitchison; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:156-164

Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions

Ahmed Alaa, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:165-174

Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions

Ahmed Alaa, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:175-190

Random extrapolation for primal-dual coordinate descent

Ahmet Alacaoglu, Olivier Fercoq, Volkan Cevher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:191-201

A new regret analysis for Adam-type algorithms

Ahmet Alacaoglu, Yura Malitsky, Panayotis Mertikopoulos, Volkan Cevher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:202-210

Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay

Reda Alami, Odalric Maillard, Raphael Feraud; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:211-221

Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

Amr Alexandari, Anshul Kundaje, Avanti Shrikumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:222-232

The Implicit Regularization of Stochastic Gradient Flow for Least Squares

Alnur Ali, Edgar Dobriban, Ryan Tibshirani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:233-244

Structural Language Models of Code

Uri Alon, Roy Sadaka, Omer Levy, Eran Yahav; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:245-256

LowFER: Low-rank Bilinear Pooling for Link Prediction

Saadullah Amin, Stalin Varanasi, Katherine Ann Dunfield, Günter Neumann; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:257-268

Discount Factor as a Regularizer in Reinforcement Learning

Ron Amit, Ron Meir, Kamil Ciosek; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:269-278

Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"

Saeed Amizadeh, Hamid Palangi, Alex Polozov, Yichen Huang, Kazuhito Koishida; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:279-290

The Differentiable Cross-Entropy Method

Brandon Amos, Denis Yarats; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:291-302

Customizing ML Predictions for Online Algorithms

Keerti Anand, Rong Ge, Debmalya Panigrahi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:303-313

Fairwashing explanations with off-manifold detergent

Christopher Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:314-323

Population-Based Black-Box Optimization for Biological Sequence Design

Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D Sculley; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:324-334

Low-loss connection of weight vectors: distribution-based approaches

Ivan Anokhin, Dmitry Yarotsky; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:335-344

Online metric algorithms with untrusted predictions

Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, Bertrand Simon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:345-355

NADS: Neural Architecture Distribution Search for Uncertainty Awareness

Randy Ardywibowo, Shahin Boluki, Xinyu Gong, Zhangyang Wang, Xiaoning Qian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:356-366

Provable Representation Learning for Imitation Learning via Bi-level Optimization

Sanjeev Arora, Simon Du, Sham Kakade, Yuping Luo, Nikunj Saunshi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:367-376

Quantum Boosting

Srinivasan Arunachalam, Reevu Maity; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:377-387

Black-box Certification and Learning under Adversarial Perturbations

Hassan Ashtiani, Vinayak Pathak, Ruth Urner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:388-398

Invertible generative models for inverse problems: mitigating representation error and dataset bias

Muhammad Asim, Max Daniels, Oscar Leong, Ali Ahmed, Paul Hand; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:399-409

On the Convergence of Nesterov’s Accelerated Gradient Method in Stochastic Settings

Mahmoud Assran, Mike Rabbat; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:410-420

Safe screening rules for L0-regression from Perspective Relaxations

Alper Atamturk, Andres Gomez; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:421-430

Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks

Pranjal Awasthi, Natalie Frank, Mehryar Mohri; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:431-441

Sample Amplification: Increasing Dataset Size even when Learning is Impossible

Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:442-451

Sparse Convex Optimization via Adaptively Regularized Hard Thresholding

Kyriakos Axiotis, Maxim Sviridenko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:452-462

Model-Based Reinforcement Learning with Value-Targeted Regression

Alex Ayoub, Zeyu Jia, Csaba Szepesvari, Mengdi Wang, Lin Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:463-474

Forecasting Sequential Data Using Consistent Koopman Autoencoders

Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael Mahoney; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:475-485

Constant Curvature Graph Convolutional Networks

Gregor Bachmann, Gary Becigneul, Octavian Ganea; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:486-496

Scalable Nearest Neighbor Search for Optimal Transport

Arturs Backurs, Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:497-506

Agent57: Outperforming the Atari Human Benchmark

Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Charles Blundell; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:507-517

Fiduciary Bandits

Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:518-527

Learning De-biased Representations with Biased Representations

Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, Seong Joon Oh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:528-539

Deep k-NN for Noisy Labels

Dara Bahri, Heinrich Jiang, Maya Gupta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:540-550

Provable Self-Play Algorithms for Competitive Reinforcement Learning

Yu Bai, Chi Jin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:551-560

Sparse Subspace Clustering with Entropy-Norm

Liang Bai, Jiye Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:561-568

Coresets for Clustering in Graphs of Bounded Treewidth

Daniel Baker, Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:569-579

Refined bounds for algorithm configuration: The knife-edge of dual class approximability

Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:580-590

Ready Policy One: World Building Through Active Learning

Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:591-601

Stochastic Optimization for Regularized Wasserstein Estimators

Marin Ballu, Quentin Berthet, Francis Bach; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:602-612

Dual Mirror Descent for Online Allocation Problems

Santiago Balseiro, Haihao Lu, Vahab Mirrokni; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:613-628

Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters

Subho Banerjee, Saurabh Jha, Zbigniew Kalbarczyk, Ravishankar Iyer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:629-641

UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Jianfeng Gao, Songhao Piao, Ming Zhou, Hsiao-Wuen Hon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:642-652

Fast OSCAR and OWL Regression via Safe Screening Rules

Runxue Bao, Bin Gu, Heng Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:653-663

Option Discovery in the Absence of Rewards with Manifold Analysis

Amitay Bar, Ronen Talmon, Ron Meir; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:664-674

Learning the piece-wise constant graph structure of a varying Ising model

Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:675-684

Frequency Bias in Neural Networks for Input of Non-Uniform Density

Ronen Basri, Meirav Galun, Amnon Geifman, David Jacobs, Yoni Kasten, Shira Kritchman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:685-694

Private Query Release Assisted by Public Data

Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, Steven Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:695-703

ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications

Kinjal Basu, Amol Ghoting, Rahul Mazumder, Yao Pan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:704-714

On Second-Order Group Influence Functions for Black-Box Predictions

Samyadeep Basu, Xuchen You, Soheil Feizi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:715-724

Kernel interpolation with continuous volume sampling

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:725-735

Decoupled Greedy Learning of CNNs

Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:736-745

The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers

Pierre Bellec, Dana Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:746-755

Defense Through Diverse Directions

Christopher Bender, Yang Li, Yifeng Shi, Michael K. Reiter, Junier Oliva; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:756-766

Interference and Generalization in Temporal Difference Learning

Emmanuel Bengio, Joelle Pineau, Doina Precup; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:767-777

Preselection Bandits

Viktor Bengs, Eyke Hüllermeier; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:778-787

Efficient Policy Learning from Surrogate-Loss Classification Reductions

Andrew Bennett, Nathan Kallus; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:788-798

Training Neural Networks for and by Interpolation

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:799-809

Implicit differentiation of Lasso-type models for hyperparameter optimization

Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:810-821

Online Learning with Imperfect Hints

Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:822-831

When are Non-Parametric Methods Robust?

Robi Bhattacharjee, Kamalika Chaudhuri; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:832-841

Learning and Sampling of Atomic Interventions from Observations

Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, Vinodchandran N. Variyam; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:842-853

Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures

Chiranjib Bhattacharyya, Ravindran Kannan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:854-863

Low-Rank Bottleneck in Multi-head Attention Models

Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:864-873

Spectral Clustering with Graph Neural Networks for Graph Pooling

Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:874-883

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders

Ioana Bica, Ahmed Alaa, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:884-895

Adversarial Robustness for Code

Pavol Bielik, Martin Vechev; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:896-907

The Boomerang Sampler

Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani, Gareth Roberts; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:908-918

Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance

Blair Bilodeau, Dylan Foster, Daniel Roy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:919-929

My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits

Ilai Bistritz, Tavor Baharav, Amir Leshem, Nicholas Bambos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:930-940

Provable guarantees for decision tree induction: the agnostic setting

Guy Blanc, Jane Lange, Li-Yang Tan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:941-949

Fast Differentiable Sorting and Ranking

Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:950-959

Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?

Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:960-969

Modulating Surrogates for Bayesian Optimization

Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill Campbell, Carl Henrik Ek; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:970-979

Deep Coordination Graphs

Wendelin Boehmer, Vitaly Kurin, Shimon Whiteson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:980-991

Lorentz Group Equivariant Neural Network for Particle Physics

Alexander Bogatskiy, Brandon Anderson, Jan Offermann, Marwah Roussi, David Miller, Risi Kondor; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:992-1002

Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More

Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1003-1013

Proper Network Interpretability Helps Adversarial Robustness in Classification

Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1014-1023

Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks

Blake Bordelon, Abdulkadir Canatar, Cengiz Pehlevan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1024-1034

Small Data, Big Decisions: Model Selection in the Small-Data Regime

Jorg Bornschein, Francesco Visin, Simon Osindero; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1035-1044

Latent Variable Modelling with Hyperbolic Normalizing Flows

Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, Will Hamilton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1045-1055

Tightening Exploration in Upper Confidence Reinforcement Learning

Hippolyte Bourel, Odalric Maillard, Mohammad Sadegh Talebi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1056-1066

Preference Modeling with Context-Dependent Salient Features

Amanda Bower, Laura Balzano; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1067-1077

Adversarial Filters of Dataset Biases

Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew Peters, Ashish Sabharwal, Yejin Choi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1078-1088

Calibration, Entropy Rates, and Memory in Language Models

Mark Braverman, Xinyi Chen, Sham Kakade, Karthik Narasimhan, Cyril Zhang, Yi Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1089-1099

Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension

Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1100-1110

All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference

Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1111-1122

Estimating the Number and Effect Sizes of Non-null Hypotheses

Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1123-1133

The FAST Algorithm for Submodular Maximization

Adam Breuer, Eric Balkanski, Yaron Singer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1134-1143

GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation

Marc Brockschmidt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1144-1152

TaskNorm: Rethinking Batch Normalization for Meta-Learning

John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard Turner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1153-1164

Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences

Daniel Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1165-1177

A Pairwise Fair and Community-preserving Approach to k-Center Clustering

Brian Brubach, Darshan Chakrabarti, John Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1178-1189

Scalable Exact Inference in Multi-Output Gaussian Processes

Wessel Bruinsma, Eric Perim, William Tebbutt, Scott Hosking, Arno Solin, Richard Turner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1190-1201

Online Pricing with Offline Data: Phase Transition and Inverse Square Law

Jinzhi Bu, David Simchi-Levi, Yunzong Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1202-1210

Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models

Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David Sontag; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1211-1219

DeBayes: a Bayesian Method for Debiasing Network Embeddings

Maarten Buyl, Tijl De Bie; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1220-1229

Structured Prediction with Partial Labelling through the Infimum Loss

Vivien Cabannnes, Alessandro Rudi, Francis Bach; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1230-1239

Online Learned Continual Compression with Adaptive Quantization Modules

Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1240-1250

Boosted Histogram Transform for Regression

Yuchao Cai, Hanyuan Hang, Hanfang Yang, Zhouchen Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1251-1261

On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies

Hengrui Cai, Wenbin Lu, Rui Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1262-1270

Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality

Changxiao Cai, H. Vincent Poor, Yuxin Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1271-1282

Provably Efficient Exploration in Policy Optimization

Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1283-1294

Near-linear time Gaussian process optimization with adaptive batching and resparsification

Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1295-1305

Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates

Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1306-1316

Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills

Victor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giro-I-Nieto, Jordi Torres; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1317-1327

Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently

Asaf Cassel, Alon Cohen, Tomer Koren; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1328-1337

Fully Parallel Hyperparameter Search: Reshaped Space-Filling

Marie-Liesse Cauwet, Camille Couprie, Julien Dehos, Pauline Luc, Jeremy Rapin, Morgane Riviere, Fabien Teytaud, Olivier Teytaud, Nicolas Usunier; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1338-1348

Data preprocessing to mitigate bias: A maximum entropy based approach

L. Elisa Celis, Vijay Keswani, Nisheeth Vishnoi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1349-1359

Meta-learning with Stochastic Linear Bandits

Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1360-1370

Description Based Text Classification with Reinforcement Learning

Duo Chai, Wei Wu, Qinghong Han, Fei Wu, Jiwei Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1371-1382

Concise Explanations of Neural Networks using Adversarial Training

Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Xi Wu, Somesh Jha; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1383-1391

Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift

Alex Chan, Ahmed Alaa, Zhaozhi Qian, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1392-1402

Imputer: Sequence Modelling via Imputation and Dynamic Programming

William Chan, Chitwan Saharia, Geoffrey Hinton, Mohammad Norouzi, Navdeep Jaitly; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1403-1413

Optimizing for the Future in Non-Stationary MDPs

Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip Thomas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1414-1425

Learning to Simulate and Design for Structural Engineering

Kai-Hung Chang, Chin-Yi Cheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1426-1436

Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions

Michael Chang, Sid Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1437-1447

Invariant Rationalization

Shiyu Chang, Yang Zhang, Mo Yu, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1448-1458

Circuit-Based Intrinsic Methods to Detect Overfitting

Satrajit Chatterjee, Alan Mishchenko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1459-1468

Better depth-width trade-offs for neural networks through the lens of dynamical systems

Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1469-1478

Explainable and Discourse Topic-aware Neural Language Understanding

Yatin Chaudhary, Hinrich Schuetze, Pankaj Gupta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1479-1488

Uncertainty-Aware Lookahead Factor Models for Quantitative Investing

Lakshay Chauhan, John Alberg, Zachary Lipton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1489-1499

Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning

Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John Gregoire, Carla Gomes; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1500-1509

Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1510-1519

Learning To Stop While Learning To Predict

Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1520-1530

Combinatorial Pure Exploration for Dueling Bandit

Wei Chen, Yihan Du, Longbo Huang, Haoyu Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1531-1541

Graph Optimal Transport for Cross-Domain Alignment

Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, Jingjing Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1542-1553

Stabilizing Differentiable Architecture Search via Perturbation-based Regularization

Xiangning Chen, Cho-Jui Hsieh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1554-1565

Mapping natural-language problems to formal-language solutions using structured neural representations

Kezhen Chen, Qiuyuan Huang, Hamid Palangi, Paul Smolensky, Ken Forbus, Jianfeng Gao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1566-1575

Convolutional Kernel Networks for Graph-Structured Data

Dexiong Chen, Laurent Jacob, Julien Mairal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1576-1586

Learning Flat Latent Manifolds with VAEs

Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick Van Der Smagt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1587-1596

A Simple Framework for Contrastive Learning of Visual Representations

Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1597-1607

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search

Binghong Chen, Chengtao Li, Hanjun Dai, Le Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1608-1616

Differentiable Product Quantization for End-to-End Embedding Compression

Ting Chen, Lala Li, Yizhou Sun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1617-1626

On Efficient Constructions of Checkpoints

Yu Chen, Zhenming Liu, Bin Ren, Xin Jin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1627-1636

Angular Visual Hardness

Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1637-1648

Estimating the Error of Randomized Newton Methods: A Bootstrap Approach

Jessie X.T. Chen, Miles Lopes; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1649-1659

VFlow: More Expressive Generative Flows with Variational Data Augmentation

Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1660-1669

More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models

Lin Chen, Yifei Min, Mingrui Zhang, Amin Karbasi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1670-1680

An Accelerated DFO Algorithm for Finite-sum Convex Functions

Yuwen Chen, Antonio Orvieto, Aurelien Lucchi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1681-1690

Generative Pretraining From Pixels

Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1691-1703

Negative Sampling in Semi-Supervised learning

John Chen, Vatsal Shah, Anastasios Kyrillidis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1704-1714

Optimization from Structured Samples for Coverage Functions

Wei Chen, Xiaoming Sun, Jialin Zhang, Zhijie Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1715-1724

Simple and Deep Graph Convolutional Networks

Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1725-1735

On Breaking Deep Generative Model-based Defenses and Beyond

Yanzhi Chen, Renjie Xie, Zhanxing Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1736-1745

Automated Synthetic-to-Real Generalization

Wuyang Chen, Zhiding Yu, Zhangyang Wang, Animashree Anandkumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1746-1756

(Locally) Differentially Private Combinatorial Semi-Bandits

Xiaoyu Chen, Kai Zheng, Zixin Zhou, Yunchang Yang, Wei Chen, Liwei Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1757-1767

High-dimensional Robust Mean Estimation via Gradient Descent

Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1768-1778

CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, Lawrence Carin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1779-1788

Learning with Bounded Instance and Label-dependent Label Noise

Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, Dacheng Tao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1789-1799

Mutual Transfer Learning for Massive Data

Ching-Wei Cheng, Xingye Qiao, Guang Cheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1800-1809

Stochastic Gradient and Langevin Processes

Xiang Cheng, Dong Yin, Peter Bartlett, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1810-1819

Representation Learning via Adversarially-Contrastive Optimal Transport

Anoop Cherian, Shuchin Aeron; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1820-1830

Convergence Rates of Variational Inference in Sparse Deep Learning

Badr-Eddine Chérief-Abdellatif; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1831-1842

Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism

Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1843-1854

Streaming Coresets for Symmetric Tensor Factorization

Rachit Chhaya, Jayesh Choudhari, Anirban Dasgupta, Supratim Shit; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1855-1865

On Coresets for Regularized Regression

Rachit Chhaya, Anirban Dasgupta, Supratim Shit; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1866-1876

How to Solve Fair k-Center in Massive Data Models

Ashish Chiplunkar, Sagar Kale, Sivaramakrishnan Natarajan Ramamoorthy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1877-1886

Fair Generative Modeling via Weak Supervision

Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1887-1898

Encoding Musical Style with Transformer Autoencoders

Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1899-1908

k-means++: few more steps yield constant approximation

Davin Choo, Christoph Grunau, Julian Portmann, Vaclav Rozhon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1909-1917

Stochastic Flows and Geometric Optimization on the Orthogonal Group

Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamas Sarlos, Adrian Weller, Vikas Sindhwani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1918-1928

Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels

Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1929-1938

Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models

Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1939-1951

Online Continual Learning from Imbalanced Data

Aristotelis Chrysakis, Marie-Francine Moens; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1952-1961

Distance Metric Learning with Joint Representation Diversification

Xu Chu, Yang Lin, Yasha Wang, Xiting Wang, Hailong Yu, Xin Gao, Qi Tong; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1962-1973

Semismooth Newton Algorithm for Efficient Projections onto $\ell_1, ∞$-norm Ball

Dejun Chu, Changshui Zhang, Shiliang Sun, Qing Tao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1974-1983

Estimating Generalization under Distribution Shifts via Domain-Invariant Representations

Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1984-1994

Scalable and Efficient Comparison-based Search without Features

Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1995-2005

Feature-map-level Online Adversarial Knowledge Distillation

Inseop Chung, Seonguk Park, Jangho Kim, Nojun Kwak; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2006-2015

Teaching with Limited Information on the Learner’s Behaviour

Ferdinando Cicalese, Sergio Filho, Eduardo Laber, Marco Molinaro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2016-2026

Deep Divergence Learning

Hatice Kubra Cilingir, Rachel Manzelli, Brian Kulis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2027-2037

Model Fusion with Kullback-Leibler Divergence

Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2038-2047

Leveraging Procedural Generation to Benchmark Reinforcement Learning

Karl Cobbe, Chris Hesse, Jacob Hilton, John Schulman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2048-2056

Composable Sketches for Functions of Frequencies: Beyond the Worst Case

Edith Cohen, Ofir Geri, Rasmus Pagh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2057-2067

Healing Products of Gaussian Process Experts

Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Deisenroth; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2068-2077

On Efficient Low Distortion Ultrametric Embedding

Vincent Cohen-Addad, Karthik C. S., Guillaume Lagarde; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2078-2088

Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data

Benjamin Coleman, Richard Baraniuk, Anshumali Shrivastava; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2089-2099

Word-Level Speech Recognition With a Letter to Word Encoder

Ronan Collobert, Awni Hannun, Gabriel Synnaeve; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2100-2110

Boosting Frank-Wolfe by Chasing Gradients

Cyrille Combettes, Sebastian Pokutta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2111-2121

Learning Opinions in Social Networks

Vincent Conitzer, Debmalya Panigrahi, Hanrui Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2122-2132

Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows

Rob Cornish, Anthony Caterini, George Deligiannidis, Arnaud Doucet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2133-2143

Adaptive Region-Based Active Learning

Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2144-2153

Online Learning with Dependent Stochastic Feedback Graphs

Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2154-2163

Learnable Group Transform For Time-Series

Romain Cosentino, Behnaam Aazhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2164-2173

DINO: Distributed Newton-Type Optimization Method

Rixon Crane, Fred Roosta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2174-2184

Causal Modeling for Fairness In Dynamical Systems

Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2185-2195

Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack

Francesco Croce, Matthias Hein; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2196-2205

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Francesco Croce, Matthias Hein; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2206-2216

Real-Time Optimisation for Online Learning in Auctions

Lorenzo Croissant, Marc Abeille, Clement Calauzenes; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2217-2226

Privately detecting changes in unknown distributions

Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2227-2237

Flexible and Efficient Long-Range Planning Through Curious Exploration

Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin Feigelis, Daniel Yamins; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2238-2249

Parameter-free, Dynamic, and Strongly-Adaptive Online Learning

Ashok Cutkosky; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2250-2259

Momentum Improves Normalized SGD

Ashok Cutkosky, Harsh Mehta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2260-2268

Supervised Quantile Normalization for Low Rank Matrix Factorization

Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2269-2279

Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime

Stéphane D’Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2280-2290

R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games

Zhongxiang Dai, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2291-2301

Scalable Deep Generative Modeling for Sparse Graphs

Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2302-2312

The Usual Suspects? Reassessing Blame for VAE Posterior Collapse

Bin Dai, Ziyu Wang, David Wipf; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2313-2322

Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting

Niccolo Dalmasso, Rafael Izbicki, Ann Lee; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2323-2334

Goodness-of-Fit Tests for Inhomogeneous Random Graphs

Soham Dan, Bhaswar B. Bhattacharya; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2335-2344

Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification

Chen Dan, Yuting Wei, Pradeep Ravikumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2345-2355

Adversarial Attacks on Probabilistic Autoregressive Forecasting Models

Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2356-2365

Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors

Yehuda Dar, Paul Mayer, Lorenzo Luzi, Richard Baraniuk; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2366-2375

Probing Emergent Semantics in Predictive Agents via Question Answering

Abhishek Das, Federico Carnevale, Hamza Merzic, Laura Rimell, Rosalia Schneider, Josh Abramson, Alden Hung, Arun Ahuja, Stephen Clark, Greg Wayne, Felix Hill; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2376-2391

Low-Variance and Zero-Variance Baselines for Extensive-Form Games

Trevor Davis, Martin Schmid, Michael Bowling; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2392-2401

Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction

Filipe De Avila Belbute-Peres, Thomas Economon, Zico Kolter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2402-2411

Representing Unordered Data Using Complex-Weighted Multiset Automata

Justin DeBenedetto, David Chiang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2412-2420

An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm

Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2421-2431

Gamification of Pure Exploration for Linear Bandits

Rémy Degenne, Pierre Menard, Xuedong Shang, Michal Valko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2432-2442

Structure Adaptive Algorithms for Stochastic Bandits

Rémy Degenne, Han Shao, Wouter Koolen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2443-2452

Randomly Projected Additive Gaussian Processes for Regression

Ian Delbridge, David Bindel, Andrew Gordon Wilson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2453-2463

Interpreting Robust Optimization via Adversarial Influence Functions

Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2464-2473

Non-convex Learning via Replica Exchange Stochastic Gradient MCMC

Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2474-2483

Towards Understanding the Dynamics of the First-Order Adversaries

Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie Su; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2484-2493

Robust Pricing in Dynamic Mechanism Design

Yuan Deng, Sebastien Lahaie, Vahab Mirrokni; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2494-2503

A Swiss Army Knife for Minimax Optimal Transport

Sofien Dhouib, Ievgen Redko, Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2504-2513

Margin-aware Adversarial Domain Adaptation with Optimal Transport

Sofien Dhouib, Ievgen Redko, Carole Lartizien; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2514-2524

Enhancing Simple Models by Exploiting What They Already Know

Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2525-2534

Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence

Lijun Ding, Yingjie Fei, Qiantong Xu, Chengrun Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2535-2544

Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features

Liang Ding, Rui Tuo, Shahin Shahrampour; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2545-2555

Layered Sampling for Robust Optimization Problems

Hu Ding, Zixiu Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2556-2566

Growing Adaptive Multi-hyperplane Machines

Nemanja Djuric, Zhuang Wang, Slobodan Vucetic; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2567-2576

Inexact Tensor Methods with Dynamic Accuracies

Nikita Doikov, Yurii Nesterov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2577-2586

Provable Smoothness Guarantees for Black-Box Variational Inference

Justin Domke; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2587-2596

Optimal Differential Privacy Composition for Exponential Mechanisms

Jinshuo Dong, David Durfee, Ryan Rogers; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2597-2606

Multinomial Logit Bandit with Low Switching Cost

Kefan Dong, Yingkai Li, Qin Zhang, Yuan Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2607-2615

Towards Adaptive Residual Network Training: A Neural-ODE Perspective

Chengyu Dong, Liyuan Liu, Zichao Li, Jingbo Shang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2616-2626

On the Expressivity of Neural Networks for Deep Reinforcement Learning

Kefan Dong, Yuping Luo, Tianhe Yu, Chelsea Finn, Tengyu Ma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2627-2637

Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems

Zhe Dong, Bryan Seybold, Kevin Murphy, Hung Bui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2638-2647

Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms

Chaosheng Dong, Bo Zeng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2648-2657

The Complexity of Finding Stationary Points with Stochastic Gradient Descent

Yoel Drori, Ohad Shamir; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2658-2667

Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer

Alexey Drutsa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2668-2677

Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders

Alexey Drutsa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2678-2689

NGBoost: Natural Gradient Boosting for Probabilistic Prediction

Tony Duan, Avati Anand, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Ng, Alejandro Schuler; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2690-2700

Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation

Yaqi Duan, Zeyu Jia, Mengdi Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2701-2709

Online Bayesian Moment Matching based SAT Solver Heuristics

Haonan Duan, Saeed Nejati, George Trimponias, Pascal Poupart, Vijay Ganesh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2710-2719

Familywise Error Rate Control by Interactive Unmasking

Boyan Duan, Aaditya Ramdas, Larry Wasserman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2720-2729

Cooperative Multi-Agent Bandits with Heavy Tails

Abhimanyu Dubey, Alex ‘Sandy’ Pentland; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2730-2739

Kernel Methods for Cooperative Multi-Agent Contextual Bandits

Abhimanyu Dubey, Alex ‘Sandy’ Pentland; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2740-2750

Optimization Theory for ReLU Neural Networks Trained with Normalization Layers

Yonatan Dukler, Quanquan Gu, Guido Montufar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2751-2760

Equivariant Neural Rendering

Emilien Dupont, Miguel Bautista Martin, Alex Colburn, Aditya Sankar, Josh Susskind, Qi Shan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2761-2770

On Contrastive Learning for Likelihood-free Inference

Conor Durkan, Iain Murray, George Papamakarios; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2771-2781

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2782-2792

Sparse Gaussian Processes with Spherical Harmonic Features

Vincent Dutordoir, Nicolas Durrande, James Hensman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2793-2802

Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing

Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush Varshney; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2803-2813

Self-Concordant Analysis of Frank-Wolfe Algorithms

Pavel Dvurechensky, Petr Ostroukhov, Kamil Safin, Shimrit Shtern, Mathias Staudigl; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2814-2824

Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients

Ashley Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2825-2835

Training Linear Neural Networks: Non-Local Convergence and Complexity Results

Armin Eftekhari; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2836-2847

Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location

Rasheed El-Bouri, David Eyre, Peter Watkinson, Tingting Zhu, David Clifton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2848-2857

Decision Trees for Decision-Making under the Predict-then-Optimize Framework

Adam Elmachtoub, Jason Cheuk Nam Liang, Ryan Mcnellis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2858-2867

Revisiting Spatial Invariance with Low-Rank Local Connectivity

Gamaleldin Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2868-2879

Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks

Ahmed Taha Elthakeb, Prannoy Pilligundla, Fatemeh Mireshghallah, Alexander Cloninger, Hadi Esmaeilzadeh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2880-2891

Generalization Error of Generalized Linear Models in High Dimensions

Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson Fletcher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2892-2901

Parallel Algorithm for Non-Monotone DR-Submodular Maximization

Alina Ene, Huy Nguyen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2902-2911

Continuous Time Bayesian Networks with Clocks

Nicolai Engelmann, Dominik Linzner, Heinz Koeppl; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2912-2921

Identifying Statistical Bias in Dataset Replication

Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2922-2932

Distributed Online Optimization over a Heterogeneous Network with Any-Batch Mirror Descent

Nima Eshraghi, Ben Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2933-2942

Rigging the Lottery: Making All Tickets Winners

Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2943-2952

Faster Graph Embeddings via Coarsening

Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2953-2963

Latent Bernoulli Autoencoder

Jiri Fajtl, Vasileios Argyriou, Dorothy Monekosso, Paolo Remagnino; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2964-2974

Optimal Sequential Maximization: One Interview is Enough!

Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2975-2984

Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory

Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2985-2995

On hyperparameter tuning in general clustering problemsm

Xinjie Fan, Yuguang Yue, Purnamrita Sarkar, Y. X. Rachel Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2996-3007

Online mirror descent and dual averaging: keeping pace in the dynamic case

Huang Fang, Nick Harvey, Victor Portella, Michael Friedlander; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3008-3017

Stochastic Regret Minimization in Extensive-Form Games

Gabriele Farina, Christian Kroer, Tuomas Sandholm; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3018-3028

Do GANs always have Nash equilibria?

Farzan Farnia, Asuman Ozdaglar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3029-3039

Growing Action Spaces

Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier, Gabriel Synnaeve; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3040-3051

Improved Optimistic Algorithms for Logistic Bandits

Louis Faury, Marc Abeille, Clement Calauzenes, Olivier Fercoq; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3052-3060

Revisiting Fundamentals of Experience Replay

William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3061-3071

Learning with Multiple Complementary Labels

Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3072-3081

Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models

Yiding Feng, Ekaterina Khmelnitskaya, Denis Nekipelov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3082-3091

The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation

Zhe Feng, David Parkes, Haifeng Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3092-3101

Accountable Off-Policy Evaluation With Kernel Bellman Statistics

Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3102-3111

Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data

Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3112-3122

Why Are Learned Indexes So Effective?

Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3123-3132

Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study

Tanner Fiez, Benjamin Chasnov, Lillian Ratliff; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3133-3144

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Angelos Filos, Panagiotis Tigkas, Rowan Mcallister, Nicholas Rhinehart, Sergey Levine, Yarin Gal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3145-3153

How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization

Chris Finlay, Joern-Henrik Jacobsen, Levon Nurbekyan, Adam Oberman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3154-3164

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data

Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3165-3176

Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains

Johannes Fischer, Ömer Sahin Tas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3177-3187

Topic Modeling via Full Dependence Mixtures

Dan Fisher, Mark Kozdoba, Shie Mannor; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3188-3198

Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles

Dylan Foster, Alexander Rakhlin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3199-3210

Logarithmic Regret for Adversarial Online Control

Dylan Foster, Max Simchowitz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3211-3221

p-Norm Flow Diffusion for Local Graph Clustering

Kimon Fountoulakis, Di Wang, Shenghao Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3222-3232

Stochastic Latent Residual Video Prediction

Jean-Yves Franceschi, Edouard Delasalles, Mickael Chen, Sylvain Lamprier, Patrick Gallinari; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3233-3246

Leveraging Frequency Analysis for Deep Fake Image Recognition

Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, Thorsten Holz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3247-3258

Linear Mode Connectivity and the Lottery Ticket Hypothesis

Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3259-3269

No-Regret and Incentive-Compatible Online Learning

Rupert Freeman, David Pennock, Chara Podimata, Jennifer Wortman Vaughan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3270-3279

Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, Christopher Re; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3280-3291

AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks

Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, Zhangyang Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3292-3303

Don’t Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript

Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao, Ce Zhang, Bin Cui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3304-3314

DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths

Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng, Yuan Yao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3315-3326

Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions

Kaito Fujii; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3327-3336

Accelerating the diffusion-based ensemble sampling by non-reversible dynamics

Futoshi Futami, Issei Sato, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3337-3347

Stochastic bandits with arm-dependent delays

Manegueu Anne Gael, Claire Vernade, Alexandra Carpentier, Michal Valko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3348-3356

Abstraction Mechanisms Predict Generalization in Deep Neural Networks

Alex Gain, Hava Siegelmann; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3357-3366

A Free-Energy Principle for Representation Learning

Yansong Gao, Pratik Chaudhari; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3367-3376

Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?

Hongchang Gao, Heng Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3377-3386

Online Convex Optimization in the Random Order Model

Dan Garber, Gal Korcia, Kfir Levy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3387-3396

Symbolic Network: Generalized Neural Policies for Relational MDPs

Sankalp Garg, Aniket Bajpai, Mausam ; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3397-3407

Predicting deliberative outcomes

Vikas Garg, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3408-3418

Generalization and Representational Limits of Graph Neural Networks

Vikas Garg, Stefanie Jegelka, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3419-3430

Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions

Sinong Geng, Houssam Nassif, Carlos Manzanares, Max Reppen, Ronnie Sircar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3431-3441

Multilinear Latent Conditioning for Generating Unseen Attribute Combinations

Markos Georgopoulos, Grigorios Chrysos, Maja Pantic, Yannis Panagakis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3442-3451

Generalisation error in learning with random features and the hidden manifold model

Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mezard, Lenka Zdeborova; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3452-3462

Black-Box Methods for Restoring Monotonicity

Evangelia Gergatsouli, Brendan Lucier, Christos Tzamos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3463-3473

Online Multi-Kernel Learning with Graph-Structured Feedback

Pouya M Ghari, Yanning Shen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3474-3483

Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics

Mahsa Ghasemi, Erdem Bulgur, Ufuk Topcu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3484-3493

Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs

Amiremad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3494-3504

Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead

Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3505-3514

Aligned Cross Entropy for Non-Autoregressive Machine Translation

Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer, Omer Levy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3515-3523

Gradient Temporal-Difference Learning with Regularized Corrections

Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3524-3534

A Distributional Framework For Data Valuation

Amirata Ghorbani, Michael Kim, James Zou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3535-3544

Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos

Subhroshekhar Ghosh, Krishna Balasubramanian, Xiaochuan Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3545-3555

Representations for Stable Off-Policy Reinforcement Learning

Dibya Ghosh, Marc G. Bellemare; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3556-3565

Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition

Alex Gittens, Kareem Aggour, Bülent Yener; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3566-3575

One Size Fits All: Can We Train One Denoiser for All Noise Levels?

Abhiram Gnanasambandam, Stanley Chan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3576-3586

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent

Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam Klivans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3587-3596

SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification

Tomer Golany, Kira Radinsky, Daniel Freedman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3597-3606

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks

Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3607-3616

Towards a General Theory of Infinite-Width Limits of Neural Classifiers

Eugene Golikov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3617-3626

Differentially Private Set Union

Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3627-3636

The continuous categorical: a novel simplex-valued exponential family

Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, John Cunningham; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3637-3647

Automatic Reparameterisation of Probabilistic Programs

Maria Gorinova, Dave Moore, Matthew Hoffman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3648-3657

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions

Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Celi, Emma Brunskill, Finale Doshi-Velez; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3658-3667

Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, Sarath Chandar, Yoshua Bengio; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3668-3679

Ordinal Non-negative Matrix Factorization for Recommendation

Olivier Gouvert, Thomas Oberlin, Cédric Févotte; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3680-3689

PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination

Saurabh Goyal, Anamitra Roy Choudhury, Saurabh Raje, Venkatesan Chakaravarthy, Yogish Sabharwal, Ashish Verma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3690-3699

PackIt: A Virtual Environment for Geometric Planning

Ankit Goyal, Jia Deng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3700-3710

DROCC: Deep Robust One-Class Classification

Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3711-3721

Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase

Jan Graßhoff, Alexandra Jankowski, Philipp Rostalski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3722-3731

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling

Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Richard Zemel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3732-3747

On the Iteration Complexity of Hypergradient Computation

Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3748-3758

Robust Learning with the Hilbert-Schmidt Independence Criterion

Daniel Greenfeld, Uri Shalit; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3759-3768

Monte-Carlo Tree Search as Regularized Policy Optimization

Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Remi Munos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3769-3778

Near-Tight Margin-Based Generalization Bounds for Support Vector Machines

Allan Grønlund, Lior Kamma, Kasper Green Larsen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3779-3788

Implicit Geometric Regularization for Learning Shapes

Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3789-3799

Improving the Gating Mechanism of Recurrent Neural Networks

Albert Gu, Caglar Gulcehre, Thomas Paine, Matt Hoffman, Razvan Pascanu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3800-3809

Recurrent Hierarchical Topic-Guided RNN for Language Generation

Dandan Guo, Bo Chen, Ruiying Lu, Mingyuan Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3810-3821

Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search

Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3822-3831

Certified Data Removal from Machine Learning Models

Chuan Guo, Tom Goldstein, Awni Hannun, Laurens Van Der Maaten; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3832-3842

LTF: A Label Transformation Framework for Correcting Label Shift

Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3843-3853

Learning to Branch for Multi-Task Learning

Pengsheng Guo, Chen-Yu Lee, Daniel Ulbricht; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3854-3863

Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3864-3874

Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning

Zhaohan Daniel Guo, Bernardo Avila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Remi Munos, Mohammad Gheshlaghi Azar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3875-3886

Accelerating Large-Scale Inference with Anisotropic Vector Quantization

Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3887-3896

Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data

Lan-Zhe Guo, Zhen-Yu Zhang, Yuan Jiang, Yu-Feng Li, Zhi-Hua Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3897-3906

Neural Topic Modeling with Continual Lifelong Learning

Pankaj Gupta, Yatin Chaudhary, Thomas Runkler, Hinrich Schuetze; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3907-3917

Multidimensional Shape Constraints

Maya Gupta, Erez Louidor, Oleksandr Mangylov, Nobu Morioka, Taman Narayan, Sen Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3918-3928

Retrieval Augmented Language Model Pre-Training

Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Mingwei Chang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3929-3938

Streaming Submodular Maximization under a k-Set System Constraint

Ran Haba, Ehsan Kazemi, Moran Feldman, Amin Karbasi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3939-3949

Let’s Agree to Agree: Neural Networks Share Classification Order on Real Datasets

Guy Hacohen, Leshem Choshen, Daphna Weinshall; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3950-3960

Optimal approximation for unconstrained non-submodular minimization

Marwa El Halabi, Stefanie Jegelka; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3961-3972

FedBoost: A Communication-Efficient Algorithm for Federated Learning

Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3973-3983

Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix

Insu Han, Haim Avron, Jinwoo Shin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3984-3993

DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images

Zhizhong Han, Chao Chen, Yu-Shen Liu, Matthias Zwicker; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3994-4005

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust

Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4006-4016

Training Binary Neural Networks through Learning with Noisy Supervision

Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu, Enhua Wu, Chang Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4017-4026

Stochastic Subspace Cubic Newton Method

Filip Hanzely, Nikita Doikov, Yurii Nesterov, Peter Richtarik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4027-4038

Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems

Filip Hanzely, Dmitry Kovalev, Peter Richtarik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4039-4048

Data Amplification: Instance-Optimal Property Estimation

Yi Hao, Alon Orlitsky; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4049-4059

Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising

Xiaotian Hao, Zhaoqing Peng, Yi Ma, Guan Wang, Junqi Jin, Jianye Hao, Shan Chen, Rongquan Bai, Mingzhou Xie, Miao Xu, Zhenzhe Zheng, Chuan Yu, Han Li, Jian Xu, Kun Gai; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4060-4070

Improving generalization by controlling label-noise information in neural network weights

Hrayr Harutyunyan, Kyle Reing, Greg Ver Steeg, Aram Galstyan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4071-4081

A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits

Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4082-4093

Bayesian Graph Neural Networks with Adaptive Connection Sampling

Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4094-4104

CoMic: Complementary Task Learning & Mimicry for Reusable Skills

Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4105-4115

Contrastive Multi-View Representation Learning on Graphs

Kaveh Hassani, Amir Hosein Khasahmadi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4116-4126

Nested Subspace Arrangement for Representation of Relational Data

Nozomi Hata, Shizuo Kaji, Akihiro Yoshida, Katsuki Fujisawa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4127-4137

The Tree Ensemble Layer: Differentiability meets Conditional Computation

Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4138-4148

Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation

Reinhard Heckel, Mahdi Soltanolkotabi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4149-4158

Hierarchically Decoupled Imitation For Morphological Transfer

Donald Hejna, Lerrel Pinto, Pieter Abbeel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4159-4171

Gradient-free Online Learning in Continuous Games with Delayed Rewards

Amélie Héliou, Panayotis Mertikopoulos, Zhengyuan Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4172-4181

Data-Efficient Image Recognition with Contrastive Predictive Coding

Olivier Henaff; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4182-4192

Minimax Rate for Learning From Pairwise Comparisons in the BTL Model

Julien Hendrickx, Alex Olshevsky, Venkatesh Saligrama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4193-4202

Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization

Hadrien Hendrikx, Lin Xiao, Sebastien Bubeck, Francis Bach, Laurent Massoulie; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4203-4227

Cost-Effective Interactive Attention Learning with Neural Attention Processes

Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4228-4238

Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Joeri Hermans, Volodimir Begy, Gilles Louppe; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4239-4248

Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)

Fabian Hinder, André Artelt, Barbara Hammer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4249-4259

Optimization and Analysis of the pAp@k Metric for Recommender Systems

Gaurush Hiranandani, Warut Vijitbenjaronk, Sanmi Koyejo, Prateek Jain; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4260-4270

Optimizing Dynamic Structures with Bayesian Generative Search

Minh Hoang, Carleton Kingsford; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4271-4281

Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion

Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4282-4292

Parameterized Rate-Distortion Stochastic Encoder

Quan Hoang, Trung Le, Dinh Phung; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4293-4303

Topologically Densified Distributions

Christoph Hofer, Florian Graf, Marc Niethammer, Roland Kwitt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4304-4313

Graph Filtration Learning

Christoph Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4314-4323

Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics

Matthew Hoffman, Yian Ma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4324-4341

Learning Mixtures of Graphs from Epidemic Cascades

Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4342-4352

Set Functions for Time Series

Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten Borgwardt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4353-4363

Lifted Disjoint Paths with Application in Multiple Object Tracking

Andrea Hornakova, Roberto Henschel, Bodo Rosenhahn, Paul Swoboda; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4364-4375

Infinite attention: NNGP and NTK for deep attention networks

Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4376-4386

The Non-IID Data Quagmire of Decentralized Machine Learning

Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip Gibbons; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4387-4398

“Other-Play” for Zero-Shot Coordination

Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob Foerster; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4399-4410

XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation

Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, Melvin Johnson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4411-4421

Momentum-Based Policy Gradient Methods

Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4422-4433

From Importance Sampling to Doubly Robust Policy Gradient

Jiawei Huang, Nan Jiang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4434-4443

Evaluating Lossy Compression Rates of Deep Generative Models

Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4444-4454

One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

Wenlong Huang, Igor Mordatch, Deepak Pathak; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4455-4464

Communication-Efficient Distributed PCA by Riemannian Optimization

Long-Kai Huang, Sinno Pan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4465-4474

Improving Transformer Optimization Through Better Initialization

Xiao Shi Huang, Felipe Perez, Jimmy Ba, Maksims Volkovs; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4475-4483

More Information Supervised Probabilistic Deep Face Embedding Learning

Ying Huang, Shangfeng Qiu, Wenwei Zhang, Xianghui Luo, Jinzhuo Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4484-4494

Generating Programmatic Referring Expressions via Program Synthesis

Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4495-4506

InstaHide: Instance-hiding Schemes for Private Distributed Learning

Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4507-4518

Accelerated Stochastic Gradient-free and Projection-free Methods

Feihu Huang, Lue Tao, Songcan Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4519-4530

Deep Graph Random Process for Relational-Thinking-Based Speech Recognition

Hengguan Huang, Fuzhao Xue, Hao Wang, Ye Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4531-4541

Dynamics of Deep Neural Networks and Neural Tangent Hierarchy

Jiaoyang Huang, Horng-Tzer Yau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4542-4551

Curvature-corrected learning dynamics in deep neural networks

Dongsung Huh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4552-4560

Multigrid Neural Memory

Tri Huynh, Michael Maire, Matthew Walter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4561-4571

Meta-Learning with Shared Amortized Variational Inference

Ekaterina Iakovleva, Jakob Verbeek, Karteek Alahari; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4572-4582

Linear Lower Bounds and Conditioning of Differentiable Games

Adam Ibrahim, Waı̈ss Azizian, Gauthier Gidel, Ioannis Mitliagkas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4583-4593

Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance

Yasutoshi Ida, Sekitoshi Kanai, Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, Hisashi Kashima; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4594-4603

Do We Need Zero Training Loss After Achieving Zero Training Error?

Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4604-4614

Semi-Supervised Learning with Normalizing Flows

Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4615-4630

Implicit Regularization of Random Feature Models

Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clement Hongler, Franck Gabriel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4631-4640

Correlation Clustering with Asymmetric Classification Errors

Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4641-4650

Optimal Robust Learning of Discrete Distributions from Batches

Ayush Jain, Alon Orlitsky; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4651-4660

Generalization to New Actions in Reinforcement Learning

Ayush Jain, Andrew Szot, Joseph Lim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4661-4672

Tails of Lipschitz Triangular Flows

Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus Brubaker; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4673-4681

Learning Portable Representations for High-Level Planning

Steven James, Benjamin Rosman, George Konidaris; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4682-4691

Debiased Sinkhorn barycenters

Hicham Janati, Marco Cuturi, Alexandre Gramfort; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4692-4701

Parametric Gaussian Process Regressors

Martin Jankowiak, Geoff Pleiss, Jacob Gardner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4702-4712

Inverse Active Sensing: Modeling and Understanding Timely Decision-Making

Daniel Jarrett, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4713-4723

Source Separation with Deep Generative Priors

Vivek Jayaram, John Thickstun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4724-4735

Extra-gradient with player sampling for faster convergence in n-player games

Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4736-4745

T-GD: Transferable GAN-generated Images Detection Framework

Hyeonseong Jeon, Young Oh Bang, Junyaup Kim, Simon Woo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4746-4761

History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms

Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4762-4772

Information-Theoretic Local Minima Characterization and Regularization

Zhiwei Jia, Hao Su; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4773-4783

Optimizing Black-box Metrics with Adaptive Surrogates

Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya Gupta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4784-4793

BINOCULARS for efficient, nonmyopic sequential experimental design

Shali Jiang, Henry Chai, Javier Gonzalez, Roman Garnett; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4794-4803

Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels

Lu Jiang, Di Huang, Mason Liu, Weilong Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4804-4815

Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation

Xiang Jiang, Qicheng Lao, Stan Matwin, Mohammad Havaei; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4816-4827

Associative Memory in Iterated Overparameterized Sigmoid Autoencoders

Yibo Jiang, Cengiz Pehlevan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4828-4838

Hierarchical Generation of Molecular Graphs using Structural Motifs

Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4839-4848

Multi-Objective Molecule Generation using Interpretable Substructures

Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4849-4859

Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition

Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4860-4869

Reward-Free Exploration for Reinforcement Learning

Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4870-4879

What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

Chi Jin, Praneeth Netrapalli, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4880-4889

Efficiently Solving MDPs with Stochastic Mirror Descent

Yujia Jin, Aaron Sidford; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4890-4900

Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model

Ying Jin, Zhaoran Wang, Junwei Lu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4901-4910

AdaScale SGD: A User-Friendly Algorithm for Distributed Training

Tyler Johnson, Pulkit Agrawal, Haijie Gu, Carlos Guestrin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4911-4920

Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization

Rie Johnson, Tong Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4921-4930

On Relativistic f-Divergences

Alexia Jolicoeur-Martineau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4931-4939

Fair k-Centers via Maximum Matching

Matthew Jones, Huy Nguyen, Thy Nguyen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4940-4949

Being Bayesian about Categorical Probability

Taejong Joo, Uijung Chung, Min-Gwan Seo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4950-4961

Evaluating the Performance of Reinforcement Learning Algorithms

Scott Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip Thomas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4962-4973

Stochastic Differential Equations with Variational Wishart Diffusions

Martin Jørgensen, Marc Deisenroth, Hugh Salimbeni; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4974-4983

A simpler approach to accelerated optimization: iterative averaging meets optimism

Pooria Joulani, Anant Raj, Andras Gyorgy, Csaba Szepesvari; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4984-4993

Sets Clustering

Ibrahim Jubran, Murad Tukan, Alaa Maalouf, Dan Feldman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4994-5005

Distribution Augmentation for Generative Modeling

Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5006-5019

Sub-Goal Trees a Framework for Goal-Based Reinforcement Learning

Tom Jurgenson, Or Avner, Edward Groshev, Aviv Tamar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5020-5030

Partial Trace Regression and Low-Rank Kraus Decomposition

Hachem Kadri, Stephane Ayache, Riikka Huusari, Alain Rakotomamonjy, Ralaivola Liva; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5031-5041

Strategyproof Mean Estimation from Multiple-Choice Questions

Anson Kahng, Gregory Kehne, Ariel Procaccia; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5042-5052

Variational Autoencoders with Riemannian Brownian Motion Priors

Dimitrios Kalatzis, David Eklund, Georgios Arvanitidis, Soren Hauberg; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5053-5066

DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training

Nathan Kallus; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5067-5077

Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation

Nathan Kallus, Masatoshi Uehara; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5078-5088

Statistically Efficient Off-Policy Policy Gradients

Nathan Kallus, Masatoshi Uehara; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5089-5100

On the Power of Compressed Sensing with Generative Models

Akshay Kamath, Eric Price, Sushrut Karmalkar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5101-5109

Learning and Evaluating Contextual Embedding of Source Code

Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5110-5121

Operation-Aware Soft Channel Pruning using Differentiable Masks

Minsoo Kang, Bohyung Han; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5122-5131

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, Ananda Theertha Suresh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5132-5143

Non-autoregressive Machine Translation with Disentangled Context Transformer

Jungo Kasai, James Cross, Marjan Ghazvininejad, Jiatao Gu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5144-5155

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, François Fleuret; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5156-5165

Rate-distortion optimization guided autoencoder for isometric embedding in Euclidean latent space

Keizo Kato, Jing Zhou, Tomotake Sasaki, Akira Nakagawa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5166-5176

Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations

Stephen Keeley, David Zoltowski, Yiyi Yu, Spencer Smith, Jonathan Pillow; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5177-5186

Quantum Expectation-Maximization for Gaussian mixture models

Iordanis Kerenidis, Alessandro Luongo, Anupam Prakash; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5187-5197

Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems

Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5198-5208

Feature Noise Induces Loss Discrepancy Across Groups

Fereshte Khani, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5209-5219

Entropy Minimization In Emergent Languages

Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco Baroni; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5220-5230

Private Outsourced Bayesian Optimization

Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5231-5242

What can I do here? A Theory of Affordances in Reinforcement Learning

Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5243-5253

Uniform Convergence of Rank-weighted Learning

Justin Khim, Liu Leqi, Adarsh Prasad, Pradeep Ravikumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5254-5263

FACT: A Diagnostic for Group Fairness Trade-offs

Joon Sik Kim, Jiahao Chen, Ameet Talwalkar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5264-5274

Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup

Jang-Hyun Kim, Wonho Choo, Hyun Oh Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5275-5285

Domain Adaptive Imitation Learning

Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5286-5295

Variational Inference for Sequential Data with Future Likelihood Estimates

Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5296-5305

Active World Model Learning with Progress Curiosity

Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5306-5315

Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation

Steven Kleinegesse, Michael U. Gutmann; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5316-5326

Optimal Continual Learning has Perfect Memory and is NP-hard

Jeremias Knoblauch, Hisham Husain, Tom Diethe; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5327-5337

Concept Bottleneck Models

Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5338-5348

Learning Similarity Metrics for Numerical Simulations

Georg Kohl, Kiwon Um, Nils Thuerey; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5349-5360

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Jonas Köhler, Leon Klein, Frank Noe; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5361-5370

Online Learning for Active Cache Synchronization

Andrey Kolobov, Sebastien Bubeck, Julian Zimmert; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5371-5380

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian Stich; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5381-5393

Meta-learning for Mixed Linear Regression

Weihao Kong, Raghav Somani, Zhao Song, Sham Kakade, Sewoong Oh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5394-5404

SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates

Lingkai Kong, Jimeng Sun, Chao Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5405-5415

On the Sample Complexity of Adversarial Multi-Source PAC Learning

Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5416-5425

Asynchronous Coagent Networks

James Kostas, Chris Nota, Philip Thomas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5426-5435

Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

Agustinus Kristiadi, Matthias Hein, Philipp Hennig; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5436-5446

A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition

Anurag Kumar, Vamsi Ithapu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5447-5457

Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness

Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5458-5467

Understanding Self-Training for Gradual Domain Adaptation

Ananya Kumar, Tengyu Ma, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5468-5479

On Implicit Regularization in $β$-VAEs

Abhishek Kumar, Ben Poole; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5480-5490

Problems with Shapley-value-based explanations as feature importance measures

I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, Sorelle Friedler; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5491-5500

Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets

Daniel Kumor, Carlos Cinelli, Elias Bareinboim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5501-5510

Two Routes to Scalable Credit Assignment without Weight Symmetry

Daniel Kunin, Aran Nayebi, Javier Sagastuy-Brena, Surya Ganguli, Jonathan Bloom, Daniel Yamins; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5511-5521

Online Dense Subgraph Discovery via Blurred-Graph Feedback

Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5522-5532

Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks

Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, Sage Moore, Nir Shavit, Dan Alistarh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5533-5543

Soft Threshold Weight Reparameterization for Learnable Sparsity

Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, Ali Farhadi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5544-5555

Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics

Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry Vetrov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5556-5566

Principled learning method for Wasserstein distributionally robust optimization with local perturbations

Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5567-5576

Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions

Prashanth L.A., Krishna Jagannathan, Ravi Kolla; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5577-5586

Optimal Randomized First-Order Methods for Least-Squares Problems

Jonathan Lacotte, Mert Pilanci; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5587-5597

Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses

Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence D’Alché-Buc; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5598-5607

Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False

Zehua Lai, Lek-Heng Lim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5608-5617

Bidirectional Model-based Policy Optimization

Hang Lai, Jian Shen, Weinan Zhang, Yong Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5618-5627

Robust and Stable Black Box Explanations

Himabindu Lakkaraju, Nino Arsov, Osbert Bastani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5628-5638

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

Michael Laskin, Aravind Srinivas, Pieter Abbeel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5639-5650

Efficient Proximal Mapping of the 1-path-norm of Shallow Networks

Fabian Latorre, Paul Rolland, Nadav Hallak, Volkan Cevher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5651-5661

Learning with Good Feature Representations in Bandits and in RL with a Generative Model

Tor Lattimore, Csaba Szepesvari, Gellert Weisz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5662-5670

Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization

Hien Le, Nicolas Gillis, Panagiotis Patrinos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5671-5681

Self-Attentive Associative Memory

Hung Le, Truyen Tran, Svetha Venkatesh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5682-5691

Causal Effect Identifiability under Partial-Observability

Sanghack Lee, Elias Bareinboim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5692-5701

Estimating Model Uncertainty of Neural Networks in Sparse Information Form

Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5702-5713

Self-supervised Label Augmentation via Input Transformations

Hankook Lee, Sung Ju Hwang, Jinwoo Shin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5714-5724

Batch Reinforcement Learning with Hyperparameter Gradients

Byungjun Lee, Jongmin Lee, Peter Vrancx, Dongho Kim, Kee-Eung Kim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5725-5735

Accelerated Message Passing for Entropy-Regularized MAP Inference

Jonathan Lee, Aldo Pacchiano, Peter Bartlett, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5736-5746

Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning

Sang-Hyun Lee, Seung-Woo Seo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5747-5756

Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning

Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, Jinwoo Shin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5757-5766

Temporal Phenotyping using Deep Predictive Clustering of Disease Progression

Changhee Lee, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5767-5777

Tensor denoising and completion based on ordinal observations

Chanwoo Lee, Miaoyan Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5778-5788

Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks

Jiabao Lei, Kui Jia; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5789-5798

SGD Learns One-Layer Networks in WGANs

Qi Lei, Jason Lee, Alex Dimakis, Constantinos Daskalakis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5799-5808

Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent

Yunwen Lei, Yiming Ying; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5809-5819

Learning Quadratic Games on Networks

Yan Leng, Xiaowen Dong, Junfeng Wu, Alex Pentland; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5820-5830

ACFlow: Flow Models for Arbitrary Conditional Likelihoods

Yang Li, Shoaib Akbar, Junier Oliva; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5831-5841

Manifold Identification for Ultimately Communication-Efficient Distributed Optimization

Yu-Sheng Li, Wei-Lin Chiang, Ching-Pei Lee; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5842-5852

Neural Architecture Search in A Proxy Validation Loss Landscape

Yanxi Li, Minjing Dong, Yunhe Wang, Chang Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5853-5862

PENNI: Pruned Kernel Sharing for Efficient CNN Inference

Shiyu Li, Edward Hanson, Hai Li, Yiran Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5863-5873

Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability

Mingjie Li, Lingshen He, Zhouchen Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5874-5883

Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning

Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, Song-Chun Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5884-5894

Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization

Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtarik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5895-5904

On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation

Jianing Li, Yanyan Lan, Jiafeng Guo, Xueqi Cheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5905-5915

Latent Space Factorisation and Manipulation via Matrix Subspace Projection

Xiao Li, Chenghua Lin, Ruizhe Li, Chaozheng Wang, Frank Guerin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5916-5926

Visual Grounding of Learned Physical Models

Yunzhu Li, Toru Lin, Kexin Yi, Daniel Bear, Daniel Yamins, Jiajun Wu, Joshua Tenenbaum, Antonio Torralba; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5927-5936

Learning from Irregularly-Sampled Time Series: A Missing Data Perspective

Steven Cheng-Xian Li, Benjamin Marlin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5937-5946

Evolutionary Topology Search for Tensor Network Decomposition

Chao Li, Zhun Sun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5947-5957

Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers

Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, Joey Gonzalez; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5958-5968

Almost Tune-Free Variance Reduction

Bingcong Li, Lingda Wang, Georgios B. Giannakis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5969-5978

Nearly Linear Row Sampling Algorithm for Quantile Regression

Yi Li, Ruosong Wang, Lin Yang, Hanrui Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5979-5989

Temporal Logic Point Processes

Shuang Li, Lu Wang, Ruizhi Zhang, Xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, Le Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5990-6000

Input-Sparsity Low Rank Approximation in Schatten Norm

Yi Li, David Woodruff; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6001-6009

RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr

Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, Dejing Dou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6010-6019

On a projective ensemble approach to two sample test for equality of distributions

Zhimei Li, Yaowu Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6020-6027

Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

Jian Liang, Dapeng Hu, Jiashi Feng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6028-6039

Variable Skipping for Autoregressive Range Density Estimation

Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Peter Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6040-6049

Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning

Tung-Che Liang, Zhanwei Zhong, Yaas Bigdeli, Tsung-Yi Ho, Krishnendu Chakrabarty, Richard Fair; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6050-6060

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-Wei Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6061-6071

Hierarchical Verification for Adversarial Robustness

Cong Han Lim, Raquel Urtasun, Ersin Yumer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6072-6082

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems

Tianyi Lin, Chi Jin, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6083-6093

Extrapolation for Large-batch Training in Deep Learning

Tao Lin, Lingjing Kong, Sebastian Stich, Martin Jaggi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6094-6104

On the Theoretical Properties of the Network Jackknife

Qiaohui Lin, Robert Lunde, Purnamrita Sarkar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6105-6115

Handling the Positive-Definite Constraint in the Bayesian Learning Rule

Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6116-6126

InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs

Zinan Lin, Kiran Thekumparampil, Giulia Fanti, Sewoong Oh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6127-6139

Improving Generative Imagination in Object-Centric World Models

Zhixuan Lin, Yi-Fu Wu, Skand Peri, Bofeng Fu, Jindong Jiang, Sungjin Ahn; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6140-6149

Generalized and Scalable Optimal Sparse Decision Trees

Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo Seltzer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6150-6160

Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games

Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6161-6171

Time-aware Large Kernel Convolutions

Vasileios Lioutas, Yuhong Guo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6172-6183

Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling

Yao Liu, Pierre-Luc Bacon, Emma Brunskill; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6184-6193

Sparse Shrunk Additive Models

Guodong Liu, Hong Chen, Heng Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6194-6204

Boosting Deep Neural Network Efficiency with Dual-Module Inference

Liu Liu, Lei Deng, Zhaodong Chen, Yuke Wang, Shuangchen Li, Jingwei Zhang, Yihua Yang, Zhenyu Gu, Yufei Ding, Yuan Xie; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6205-6215

Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors

Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6216-6225

Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates

Yang Liu, Hongyi Guo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6226-6236

An Imitation Learning Approach for Cache Replacement

Evan Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6237-6247

Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits

Xi Liu, Ping-Chun Hsieh, Yu Heng Hung, Anirban Bhattacharya, P. Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6248-6258

Hallucinative Topological Memory for Zero-Shot Visual Planning

Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6259-6270

A Chance-Constrained Generative Framework for Sequence Optimization

Xianggen Liu, Qiang Liu, Sen Song, Jian Peng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6271-6281

Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks

Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Mingyi Hong, Una-May O’Reilly; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6282-6293

Median Matrix Completion: from Embarrassment to Optimality

Weidong Liu, Xiaojun Mao, Raymond K. W. Wong; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6294-6304

A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton

Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6305-6315

Learning Deep Kernels for Non-Parametric Two-Sample Tests

Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6316-6326

Learning to Encode Position for Transformer with Continuous Dynamical Model

Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon, Cho-Jui Hsieh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6327-6335

Finding trainable sparse networks through Neural Tangent Transfer

Tianlin Liu, Friedemann Zenke; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6336-6347

Weakly-Supervised Disentanglement Without Compromises

Francesco Locatello, Ben Poole, Gunnar Raetsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6348-6359

Too Relaxed to Be Fair

Michael Lohaus, Michael Perrot, Ulrike Von Luxburg; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6360-6369

Stochastic Hamiltonian Gradient Methods for Smooth Games

Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6370-6381

Error Estimation for Sketched SVD via the Bootstrap

Miles Lopes, N. Benjamin Erichson, Michael Mahoney; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6382-6392

Differentiating through the Fréchet Mean

Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, Christopher De Sa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6393-6403

Working Memory Graphs

Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew Hausknecht; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6404-6414

Moniqua: Modulo Quantized Communication in Decentralized SGD

Yucheng Lu, Christopher De Sa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6415-6425

A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth

Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, Lexing Ying; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6426-6436

Countering Language Drift with Seeded Iterated Learning

Yuchen Lu, Soumye Singhal, Florian Strub, Aaron Courville, Olivier Pietquin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6437-6447

Does label smoothing mitigate label noise?

Michal Lukasik, Srinadh Bhojanapalli, Aditya Menon, Sanjiv Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6448-6458

Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study

Siqiang Luo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6459-6467

Progressive Graph Learning for Open-Set Domain Adaptation

Yadan Luo, Zijian Wang, Zi Huang, Mahsa Baktashmotlagh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6468-6478

Adversarial Nonnegative Matrix Factorization

Lei Luo, Yanfu Zhang, Heng Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6479-6488

Learning Algebraic Multigrid Using Graph Neural Networks

Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6489-6499

Progressive Identification of True Labels for Partial-Label Learning

Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6500-6510

Bandits with Adversarial Scaling

Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6511-6521

Efficient Continuous Pareto Exploration in Multi-Task Learning

Pingchuan Ma, Tao Du, Wojciech Matusik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6522-6531

Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space

Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6532-6542

Normalized Loss Functions for Deep Learning with Noisy Labels

Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, James Bailey; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6543-6553

Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints

Runchao Ma, Qihang Lin, Tianbao Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6554-6564

Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle

Shaocong Ma, Yi Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6565-6574

Adversarial Neural Pruning with Latent Vulnerability Suppression

Divyam Madaan, Jinwoo Shin, Sung Ju Hwang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6575-6585

Individual Fairness for k-Clustering

Sepideh Mahabadi, Ali Vakilian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6586-6596

Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization

Debabrata Mahapatra, Vaibhav Rajan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6597-6607

How recurrent networks implement contextual processing in sentiment analysis

Niru Maheswaranathan, David Sussillo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6608-6619

Anderson Acceleration of Proximal Gradient Methods

Vien Mai, Mikael Johansson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6620-6629

Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization

Vien Mai, Mikael Johansson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6630-6639

Adversarial Robustness Against the Union of Multiple Perturbation Models

Pratyush Maini, Eric Wong, Zico Kolter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6640-6650

Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination

Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen Mcaleer, Kagan Tumer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6651-6660

Estimation of Bounds on Potential Outcomes For Decision Making

Maggie Makar, Fredrik Johansson, John Guttag, David Sontag; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6661-6671

Optimal transport mapping via input convex neural networks

Ashok Makkuva, Amirhossein Taghvaei, Sewoong Oh, Jason Lee; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6672-6681

Proving the Lottery Ticket Hypothesis: Pruning is All You Need

Eran Malach, Gilad Yehudai, Shai Shalev-Schwartz, Ohad Shamir; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6682-6691

From Local SGD to Local Fixed-Point Methods for Federated Learning

Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtarik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6692-6701

Adaptive Gradient Descent without Descent

Yura Malitsky, Konstantin Mishchenko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6702-6712

Emergence of Separable Manifolds in Deep Language Representations

Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, Sueyeon Chung; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6713-6723

Adaptive Adversarial Multi-task Representation Learning

Yuren Mao, Weiwei Liu, Xuemin Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6724-6733

On Learning Sets of Symmetric Elements

Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6734-6744

Stochastically Dominant Distributional Reinforcement Learning

John Martin, Michal Lyskawinski, Xiaohu Li, Brendan Englot; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6745-6754

Minimax Pareto Fairness: A Multi Objective Perspective

Natalia Martinez, Martin Bertran, Guillermo Sapiro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6755-6764

Predictive Multiplicity in Classification

Charles Marx, Flavio Calmon, Berk Ustun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6765-6774

Adding seemingly uninformative labels helps in low data regimes

Christos Matsoukas, Albert Bou Hernandez, Yue Liu, Karin Dembrower, Gisele Miranda, Emir Konuk, Johan Fredin Haslum, Athanasios Zouzos, Peter Lindholm, Fredrik Strand, Kevin Smith; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6775-6784

Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations

Robert Mattila, Cristian Rojas, Eric Moulines, Vikram Krishnamurthy, Bo Wahlberg; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6785-6796

On Approximate Thompson Sampling with Langevin Algorithms

Eric Mazumdar, Aldo Pacchiano, Yian Ma, Michael Jordan, Peter Bartlett; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6797-6807

Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification

Hongyuan Mei, Guanghui Qin, Minjie Xu, Jason Eisner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6808-6819

On the Global Convergence Rates of Softmax Policy Gradient Methods

Jincheng Mei, Chenjun Xiao, Csaba Szepesvari, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6820-6829

Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM

Kunal Menda, Jean De Becdelievre, Jayesh Gupta, Ilan Kroo, Mykel Kochenderfer, Zachary Manchester; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6830-6840

Randomized Block-Diagonal Preconditioning for Parallel Learning

Celestine Mendler-Dünner, Aurelien Lucchi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6841-6851

Training Binary Neural Networks using the Bayesian Learning Rule

Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6852-6861

Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning

Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6862-6873

The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture

Francesca Mignacco, Florent Krzakala, Yue Lu, Pierfrancesco Urbani, Lenka Zdeborova; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6874-6883

Projective Preferential Bayesian Optimization

Petrus Mikkola, Milica Todorović, Jari Järvi, Patrick Rinke, Samuel Kaski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6884-6892

VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing

Zoltán Milacski, Barnabas Poczos, Andras Lorincz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6893-6904

The Effect of Natural Distribution Shift on Question Answering Models

John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6905-6916

Strategic Classification is Causal Modeling in Disguise

John Miller, Smitha Milli, Moritz Hardt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6917-6926

Automatic Shortcut Removal for Self-Supervised Representation Learning

Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6927-6937

Learning Reasoning Strategies in End-to-End Differentiable Proving

Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6938-6949

Coresets for Data-efficient Training of Machine Learning Models

Baharan Mirzasoleiman, Jeff Bilmes, Jure Leskovec; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6950-6960

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6961-6971

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules

Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6972-6986

Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6987-6998

Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time

Zahra Monfared, Daniel Durstewitz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6999-7009

Efficiently Learning Adversarially Robust Halfspaces with Noise

Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7010-7021

An end-to-end approach for the verification problem: learning the right distance

Joao Monteiro, Isabela Albuquerque, Jahangir Alam, R Devon Hjelm, Tiago Falk; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7022-7033

Confidence-Aware Learning for Deep Neural Networks

Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7034-7044

Topological Autoencoders

Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7045-7054

Explainable k-Means and k-Medians Clustering

Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, Nave Frost; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7055-7065

Fair Learning with Private Demographic Data

Hussein Mozannar, Mesrob Ohannessian, Nathan Srebro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7066-7075

Consistent Estimators for Learning to Defer to an Expert

Hussein Mozannar, David Sontag; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7076-7087

Continuous-time Lower Bounds for Gradient-based Algorithms

Michael Muehlebach, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7088-7096

Two Simple Ways to Learn Individual Fairness Metrics from Data

Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7097-7107

Unique Properties of Flat Minima in Deep Networks

Rotem Mulayoff, Tomer Michaeli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7108-7118

Fast computation of Nash Equilibria in Imperfect Information Games

Remi Munos, Julien Perolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7119-7129

Missing Data Imputation using Optimal Transport

Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7130-7140

Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees

Sen Na, Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7141-7152

Full Law Identification in Graphical Models of Missing Data: Completeness Results

Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7153-7163

Voice Separation with an Unknown Number of Multiple Speakers

Eliya Nachmani, Yossi Adi, Lior Wolf; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7164-7175

Reliable Fidelity and Diversity Metrics for Generative Models

Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, Jaejun Yoo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7176-7185

From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics

Sai Ganesh Nagarajan, David Balduzzi, Georgios Piliouras; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7186-7196

Up or Down? Adaptive Rounding for Post-Training Quantization

Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, Tijmen Blankevoort; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7197-7206

Goal-Aware Prediction: Learning to Model What Matters

Suraj Nair, Silvio Savarese, Chelsea Finn; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7207-7219

PolyGen: An Autoregressive Generative Model of 3D Meshes

Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter Battaglia; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7220-7229

Bayesian Sparsification of Deep C-valued Networks

Ivan Nazarov, Evgeny Burnaev; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7230-7242

Oracle Efficient Private Non-Convex Optimization

Seth Neel, Aaron Roth, Giuseppe Vietri, Steven Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7243-7252

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization

Geoffrey Negiar, Gideon Dresdner, Alicia Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund, Fabian Pedregosa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7253-7262

In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors

Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel Roy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7263-7272

Involutive MCMC: a Unifying Framework

Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7273-7282

Aggregation of Multiple Knockoffs

Tuan-Binh Nguyen, Jerome-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7283-7293

LEEP: A New Measure to Evaluate Transferability of Learned Representations

Cuong Nguyen, Tal Hassner, Matthias Seeger, Cedric Archambeau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7294-7305

Graph Homomorphism Convolution

Hoang Nguyen, Takanori Maehara; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7306-7316

Knowing The What But Not The Where in Bayesian Optimization

Vu Nguyen, Michael A. Osborne; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7317-7326

Robust Bayesian Classification Using An Optimistic Score Ratio

Viet Anh Nguyen, Nian Si, Jose Blanchet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7327-7337

Streaming k-Submodular Maximization under Noise subject to Size Constraint

Lan Nguyen, My T. Thai; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7338-7347

LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction

Vlad Niculae, Andre Martins; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7348-7359

Semi-Supervised StyleGAN for Disentanglement Learning

Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit Patel, Animashree Anandkumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7360-7369

Supervised learning: no loss no cry

Richard Nock, Aditya Menon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7370-7380

Consistent Structured Prediction with Max-Min Margin Markov Networks

Alex Nowak, Francis Bach, Alessandro Rudi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7381-7391

T-Basis: a Compact Representation for Neural Networks

Anton Obukhov, Maxim Rakhuba, Stamatios Georgoulis, Menelaos Kanakis, Dengxin Dai, Luc Van Gool; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7392-7404

Eliminating the Invariance on the Loss Landscape of Linear Autoencoders

Reza Oftadeh, Jiayi Shen, Zhangyang Wang, Dylan Shell; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7405-7413

On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes

Naoto Ohsaka, Tatsuya Matsuoka; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7414-7423

Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?

Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, Daniel Nikovski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7424-7433

Interferometric Graph Transform: a Deep Unsupervised Graph Representation

Edouard Oyallon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7434-7444

Learning to Score Behaviors for Guided Policy Optimization

Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7445-7454

Neural Clustering Processes

Ari Pakman, Yueqi Wang, Catalin Mitelut, Jinhyung Lee, Liam Paninski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7455-7465

Recovery of Sparse Signals from a Mixture of Linear Samples

Soumyabrata Pal, Arya Mazumdar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7466-7475

Adversarial Mutual Information for Text Generation

Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7476-7486

Stabilizing Transformers for Reinforcement Learning

Emilio Parisotto, Francis Song, Jack Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant Jayakumar, Max Jaderberg, Raphaël Lopez Kaufman, Aidan Clark, Seb Noury, Matthew Botvinick, Nicolas Heess, Raia Hadsell; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7487-7498

Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis

Jung Yeon Park, Kenneth Carr, Stephan Zheng, Yisong Yue, Rose Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7499-7509

Meta Variance Transfer: Learning to Augment from the Others

Seong-Jin Park, Seungju Han, Ji-Won Baek, Insoo Kim, Juhwan Song, Hae Beom Lee, Jae-Joon Han, Sung Ju Hwang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7510-7520

Structured Policy Iteration for Linear Quadratic Regulator

Youngsuk Park, Ryan Rossi, Zheng Wen, Gang Wu, Handong Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7521-7531

Regularized Optimal Transport is Ground Cost Adversarial

François-Pierre Paty, Marco Cuturi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7532-7542

Reducing Sampling Error in Batch Temporal Difference Learning

Brahma Pavse, Ishan Durugkar, Josiah Hanna, Peter Stone; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7543-7552

Acceleration through spectral density estimation

Fabian Pedregosa, Damien Scieur; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7553-7562

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van Den Broeck, Kristian Kersting, Zoubin Ghahramani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7563-7574

Learning Selection Strategies in Buchberger’s Algorithm

Dylan Peifer, Michael Stillman, Daniel Halpern-Leistner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7575-7585

Non-Autoregressive Neural Text-to-Speech

Kainan Peng, Wei Ping, Zhao Song, Kexin Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7586-7598

Performative Prediction

Juan Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, Moritz Hardt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7599-7609

Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks

Dmytro Perekrestenko, Stephan Müller, Helmut Bölcskei; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7610-7619

Budgeted Online Influence Maximization

Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7620-7631

Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks

Adeel Pervez, Taco Cohen, Efstratios Gavves; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7632-7640

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7641-7651

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

Aleksei Petrenko, Zhehui Huang, Tushar Kumar, Gaurav Sukhatme, Vladlen Koltun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7652-7662

IPBoost – Non-Convex Boosting via Integer Programming

Marc Pfetsch, Sebastian Pokutta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7663-7672

On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm

Khiem Pham, Khang Le, Nhat Ho, Tung Pham, Hung Bui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7673-7682

Scalable Differential Privacy with Certified Robustness in Adversarial Learning

Hai Phan, My T. Thai, Han Hu, Ruoming Jin, Tong Sun, Dejing Dou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7683-7694

Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks

Mert Pilanci, Tolga Ergen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7695-7705

WaveFlow: A Compact Flow-based Model for Raw Audio

Wei Ping, Kainan Peng, Kexin Zhao, Zhao Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7706-7716

Randomization matters How to defend against strong adversarial attacks

Rafael Pinot, Raphael Ettedgui, Geovani Rizk, Yann Chevaleyre, Jamal Atif; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7717-7727

Efficient Domain Generalization via Common-Specific Low-Rank Decomposition

Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7728-7738

Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation

Konstantinos Pitas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7739-7749

Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning

Silviu Pitis, Harris Chan, Stephen Zhao, Bradly Stadie, Jimmy Ba; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7750-7761

Explaining Groups of Points in Low-Dimensional Representations

Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7762-7771

On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness

Sebastian Pokutta, Mohit Singh, Alfredo Torrico; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7772-7782

Skew-Fit: State-Covering Self-Supervised Reinforcement Learning

Vitchyr Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7783-7792

SoftSort: A Continuous Relaxation for the argsort Operator

Sebastian Prillo, Julian Eisenschlos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7793-7802

Graph-based Nearest Neighbor Search: From Practice to Theory

Liudmila Prokhorenkova, Aleksandr Shekhovtsov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7803-7813

Adversarial Risk via Optimal Transport and Optimal Couplings

Muni Sreenivas Pydi, Varun Jog; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7814-7823

Deep Isometric Learning for Visual Recognition

Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, Jitendra Malik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7824-7835

Unsupervised Speech Decomposition via Triple Information Bottleneck

Kaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson, David Cox; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7836-7846

Scalable Differentiable Physics for Learning and Control

Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7847-7856

Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis

Shuang Qiu, Xiaohan Wei, Zhuoran Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7857-7866

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

Meng Qu, Tianyu Gao, Louis-Pascal Xhonneux, Jian Tang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7867-7876

DeepCoDA: personalized interpretability for compositional health data

Thomas Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7877-7886

Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints

Akbar Rafiey, Yuichi Yoshida; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7887-7897

Transparency Promotion with Model-Agnostic Linear Competitors

Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7898-7908

Understanding and Mitigating the Tradeoff between Robustness and Accuracy

Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7909-7919

Fast Adaptation to New Environments via Policy-Dynamics Value Functions

Roberta Raileanu, Max Goldstein, Arthur Szlam, Rob Fergus; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7920-7931

Improving Robustness of Deep-Learning-Based Image Reconstruction

Ankit Raj, Yoram Bresler, Bo Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7932-7942

Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs

Aditya Rajagopal, Diederik Vink, Stylianos Venieris, Christos-Savvas Bouganis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7943-7952

A Game Theoretic Framework for Model Based Reinforcement Learning

Aravind Rajeswaran, Igor Mordatch, Vikash Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7953-7963

Closing the convergence gap of SGD without replacement

Shashank Rajput, Anant Gupta, Dimitris Papailiopoulos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7964-7973

Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning

Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7974-7984

Implicit Generative Modeling for Efficient Exploration

Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7985-7995

Universal Equivariant Multilayer Perceptrons

Siamak Ravanbakhsh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7996-8006

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

Esteban Real, Chen Liang, David So, Quoc Le; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8007-8019

Learning Human Objectives by Evaluating Hypothetical Behavior

Siddharth Reddy, Anca Dragan, Sergey Levine, Shane Legg, Jan Leike; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8020-8029

Optimistic Bounds for Multi-output Learning

Henry Reeve, Ata Kaban; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8030-8040

Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation

Florence Regol, Soumyasundar Pal, Yingxue Zhang, Mark Coates; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8041-8050

The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons

Wenbo Ren, Jia Liu, Ness Shroff; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8051-8072

NetGAN without GAN: From Random Walks to Low-Rank Approximations

Luca Rendsburg, Holger Heidrich, Ulrike Von Luxburg; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8073-8082

Normalizing Flows on Tori and Spheres

Danilo Jimenez Rezende, George Papamakarios, Sebastien Racaniere, Michael Albergo, Gurtej Kanwar, Phiala Shanahan, Kyle Cranmer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8083-8092

Overfitting in adversarially robust deep learning

Leslie Rice, Eric Wong, Zico Kolter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8093-8104

Decentralised Learning with Random Features and Distributed Gradient Descent

Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8105-8115

Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge

Laura Rieger, Chandan Singh, William Murdoch, Bin Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8116-8126

Strength from Weakness: Fast Learning Using Weak Supervision

Joshua Robinson, Stefanie Jegelka, Suvrit Sra; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8127-8136

On Semi-parametric Inference for BART

Veronika Rockova; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8137-8146

FR-Train: A Mutual Information-Based Approach to Fair and Robust Training

Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8147-8157

Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning

Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Bjorkegren, Moritz Hardt, Joshua Blumenstock; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8158-8168

Double-Loop Unadjusted Langevin Algorithm

Paul Rolland, Armin Eftekhari, Ali Kavis, Volkan Cevher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8169-8177

Reverse-engineering deep ReLU networks

David Rolnick, Konrad Kording; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8178-8187

Attentive Group Equivariant Convolutional Networks

David Romero, Erik Bekkers, Jakub Tomczak, Mark Hoogendoorn; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8188-8199

Finite-Time Convergence in Continuous-Time Optimization

Orlando Romero, Mouhacine Benosman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8200-8209

Near-optimal Regret Bounds for Stochastic Shortest Path

Aviv Rosenberg, Alon Cohen, Yishay Mansour, Haim Kaplan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8210-8219

Predicting Choice with Set-Dependent Aggregation

Nir Rosenfeld, Kojin Oshiba, Yaron Singer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8220-8229

Certified Robustness to Label-Flipping Attacks via Randomized Smoothing

Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, Zico Kolter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8230-8241

Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

Karsten Roth, Timo Milbich, Samarth Sinha, Prateek Gupta, Bjorn Ommer, Joseph Paul Cohen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8242-8252

FetchSGD: Communication-Efficient Federated Learning with Sketching

Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8253-8265

Simple and sharp analysis of k-means||

Václav Rozhoň; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8266-8275

Bayesian Optimisation over Multiple Continuous and Categorical Inputs

Binxin Ru, Ahsan Alvi, Vu Nguyen, Michael A. Osborne, Stephen Roberts; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8276-8285

Inter-domain Deep Gaussian Processes

Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8286-8294

Bio-Inspired Hashing for Unsupervised Similarity Search

Chaitanya Ryali, John Hopfield, Leopold Grinberg, Dmitry Krotov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8295-8306

Adversarial Attacks on Copyright Detection Systems

Parsa Saadatpanah, Ali Shafahi, Tom Goldstein; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8307-8315

Bounding the fairness and accuracy of classifiers from population statistics

Sivan Sabato, Elad Yom-Tov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8316-8325

Radioactive data: tracing through training

Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Herve Jegou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8326-8335

Causal Structure Discovery from Distributions Arising from Mixtures of DAGs

Basil Saeed, Snigdha Panigrahi, Caroline Uhler; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8336-8345

An Investigation of Why Overparameterization Exacerbates Spurious Correlations

Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8346-8356

Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards

Aadirupa Saha, Pierre Gaillard, Michal Valko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8357-8366

From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model

Aadirupa Saha, Aditya Gopalan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8367-8376

Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics

Debjani Saha, Candice Schumann, Duncan Mcelfresh, John Dickerson, Michelle Mazurek, Michael Tschantz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8377-8387

From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models

Aytunc Sahin, Yatao Bian, Joachim Buhmann, Andreas Krause; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8388-8397

Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models

Yuta Saito, Shota Yasui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8398-8407

Inferring DQN structure for high-dimensional continuous control

Andrey Sakryukin, Chedy Raissi, Mohan Kankanhalli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8408-8416

The Performance Analysis of Generalized Margin Maximizers on Separable Data

Fariborz Salehi, Ehsan Abbasi, Babak Hassibi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8417-8426

Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization

Sudeep Salgia, Qing Zhao, Sattar Vakili; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8427-8437

A Quantile-based Approach for Hyperparameter Transfer Learning

David Salinas, Huibin Shen, Valerio Perrone; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8438-8448

Spectral Subsampling MCMC for Stationary Time Series

Robert Salomone, Matias Quiroz, Robert Kohn, Mattias Villani, Minh-Ngoc Tran; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8449-8458

Learning to Simulate Complex Physics with Graph Networks

Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter Battaglia; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8459-8468

The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent

Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8469-8479

Explicit Gradient Learning for Black-Box Optimization

Elad Sarafian, Mor Sinay, Yoram Louzoun, Noa Agmon, Sarit Kraus; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8480-8490

Detecting Out-of-Distribution Examples with Gram Matrices

Chandramouli Shama Sastry, Sageev Oore; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8491-8501

Constrained Markov Decision Processes via Backward Value Functions

Harsh Satija, Philip Amortila, Joelle Pineau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8502-8511

A Sample Complexity Separation between Non-Convex and Convex Meta-Learning

Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8512-8521

Harmonic Decompositions of Convolutional Networks

Meyer Scetbon, Zaid Harchaoui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8522-8532

Implicit competitive regularization in GANs

Florian Schaefer, Hongkai Zheng, Animashree Anandkumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8533-8544

Off-Policy Actor-Critic with Shared Experience Replay

Simon Schmitt, Matteo Hessel, Karen Simonyan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8545-8554

Discriminative Adversarial Search for Abstractive Summarization

Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8555-8564

Universal Average-Case Optimality of Polyak Momentum

Damien Scieur, Fabian Pedregosa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8565-8572

Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures

Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain Couillet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8573-8582

Planning to Explore via Self-Supervised World Models

Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8583-8592

An Explicitly Relational Neural Network Architecture

Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David Barrett, Marta Garnelo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8593-8603

Optimistic Policy Optimization with Bandit Feedback

Lior Shani, Yonathan Efroni, Aviv Rosenberg, Shie Mannor; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8604-8613

Neural Kernels Without Tangents

Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Jonathan Ragan-Kelley, Ludwig Schmidt, Benjamin Recht; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8614-8623

Learning Robot Skills with Temporal Variational Inference

Tanmay Shankar, Abhinav Gupta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8624-8633

Evaluating Machine Accuracy on ImageNet

Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8634-8644

Channel Equilibrium Networks for Learning Deep Representation

Wenqi Shao, Shitao Tang, Xingang Pan, Ping Tan, Xiaogang Wang, Ping Luo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8645-8654

ControlVAE: Controllable Variational Autoencoder

Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, Tarek Abdelzaher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8655-8664

Lookahead-Bounded Q-learning

Ibrahim El Shar, Daniel Jiang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8665-8675

Causal Strategic Linear Regression

Yonadav Shavit, Benjamin Edelman, Brian Axelrod; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8676-8686

Adaptive Sampling for Estimating Probability Distributions

Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8687-8696

PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions

Zhengyang Shen, Lingshen He, Zhouchen Lin, Jinwen Ma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8697-8706

Deep Reinforcement Learning with Robust and Smooth Policy

Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8707-8718

Educating Text Autoencoders: Latent Representation Guidance via Denoising

Tianxiao Shen, Jonas Mueller, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8719-8729

Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints

Cong Shen, Zhiyang Wang, Sofia Villar, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8730-8740

PowerNorm: Rethinking Batch Normalization in Transformers

Sheng Shen, Zhewei Yao, Amir Gholami, Michael Mahoney, Kurt Keutzer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8741-8751

Extreme Multi-label Classification from Aggregated Labels

Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit Dhillon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8752-8762

One-shot Distributed Ridge Regression in High Dimensions

Yue Sheng, Edgar Dobriban; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8763-8772

Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks

Alexander Shevchenko, Marco Mondelli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8773-8784

Incremental Sampling Without Replacement for Sequence Models

Kensen Shi, David Bieber, Charles Sutton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8785-8795

Message Passing Least Squares Framework and its Application to Rotation Synchronization

Yunpeng Shi, Gilad Lerman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8796-8806

Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making

Chengchun Shi, Runzhe Wan, Rui Song, Wenbin Lu, Ling Leng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8807-8817

A Graph to Graphs Framework for Retrosynthesis Prediction

Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8818-8827

Informative Dropout for Robust Representation Learning: A Shape-bias Perspective

Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8828-8839

Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation

Wenxian Shi, Hao Zhou, Ning Miao, Lei Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8840-8851

On Conditional Versus Marginal Bias in Multi-Armed Bandits

Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8852-8861

Predictive Coding for Locally-Linear Control

Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung Bui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8862-8871

A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change

Salman Sadiq Shuvo, Yasin Yilmaz, Alan Bush, Mark Hafen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8872-8883

Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits

Nian Si, Fan Zhang, Zhengyuan Zhou, Jose Blanchet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8884-8894

Piecewise Linear Regression via a Difference of Convex Functions

Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8895-8904

Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards

Umer Siddique, Paul Weng, Matthieu Zimmer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8905-8915

Deep Gaussian Markov Random Fields

Per Sidén, Fredrik Lindsten; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8916-8926

Collaborative Machine Learning with Incentive-Aware Model Rewards

Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8927-8936

Naive Exploration is Optimal for Online LQR

Max Simchowitz, Dylan Foster; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8937-8948

A Generative Model for Molecular Distance Geometry

Gregor Simm, Jose Miguel Hernandez-Lobato; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8949-8958

Reinforcement Learning for Molecular Design Guided by Quantum Mechanics

Gregor Simm, Robert Pinsler, Jose Miguel Hernandez-Lobato; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8959-8969

Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise

Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gurbuzbalaban; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8970-8980

Second-Order Provable Defenses against Adversarial Attacks

Sahil Singla, Soheil Feizi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8981-8991

FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis

Aman Sinha, Matthew O’Kelly, Hongrui Zheng, Rahul Mangharam, John Duchi, Russ Tedrake; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8992-9004

Small-GAN: Speeding up GAN Training using Core-Sets

Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9005-9015

Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure

John Sipple; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9016-9025

Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis

Vidyashankar Sivakumar, Steven Wu, Arindam Banerjee; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9026-9035

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning

Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9036-9045

When Explanations Lie: Why Many Modified BP Attributions Fail

Leon Sixt, Maximilian Granz, Tim Landgraf; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9046-9057

On the Generalization Benefit of Noise in Stochastic Gradient Descent

Samuel Smith, Erich Elsen, Soham De; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9058-9067

Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation

Georgios Smyrnis, Petros Maragos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9068-9077

Bridging the Gap Between f-GANs and Wasserstein GANs

Jiaming Song, Stefano Ermon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9078-9087

Provably Efficient Model-based Policy Adaptation

Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9088-9098

Hypernetwork approach to generating point clouds

Przemysław Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zieba, Tomasz Trzcinski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9099-9108

Robustness to Spurious Correlations via Human Annotations

Megha Srivastava, Tatsunori Hashimoto, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9109-9119

Which Tasks Should Be Learned Together in Multi-task Learning?

Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, Silvio Savarese; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9120-9132

Responsive Safety in Reinforcement Learning by PID Lagrangian Methods

Adam Stooke, Joshua Achiam, Pieter Abbeel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9133-9143

Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information

Karl Stratos, Sam Wiseman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9144-9154

Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks

David Stutz, Matthias Hein, Bernt Schiele; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9155-9166

Doubly robust off-policy evaluation with shrinkage

Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9167-9176

Task Understanding from Confusing Multi-task Data

Xin Su, Yizhou Jiang, Shangqi Guo, Feng Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9177-9186

ConQUR: Mitigating Delusional Bias in Deep Q-Learning

Dijia Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9187-9195

Adaptive Estimator Selection for Off-Policy Evaluation

Yi Su, Pavithra Srinath, Akshay Krishnamurthy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9196-9205

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, Jeffrey Clune; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9206-9216

Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking

Haoran Sun, Songtao Lu, Mingyi Hong; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9217-9228

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, Moritz Hardt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9229-9248

An EM Approach to Non-autoregressive Conditional Sequence Generation

Zhiqing Sun, Yiming Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9249-9258

The Shapley Taylor Interaction Index

Mukund Sundararajan, Kedar Dhamdhere, Ashish Agarwal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9259-9268

The Many Shapley Values for Model Explanation

Mukund Sundararajan, Amir Najmi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9269-9278

Multi-objective Bayesian Optimization using Pareto-frontier Entropy

Shinya Suzuki, Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9279-9288

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks

Jakub Swiatkowski, Kevin Roth, Bastiaan Veeling, Linh Tran, Joshua Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9289-9299

Multi-Agent Routing Value Iteration Network

Quinlan Sykora, Mengye Ren, Raquel Urtasun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9300-9310

Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery

Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9311-9323

Quantized Decentralized Stochastic Learning over Directed Graphs

Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9324-9333

Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization

Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9334-9345

Fiedler Regularization: Learning Neural Networks with Graph Sparsity

Edric Tam, David Dunson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9346-9355

DropNet: Reducing Neural Network Complexity via Iterative Pruning

Chong Min John Tan, Mehul Motani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9356-9366

Reinforcement Learning for Integer Programming: Learning to Cut

Yunhao Tang, Shipra Agrawal, Yuri Faenza; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9367-9376

The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application

Wenpin Tang, Xin Guo, Fengmin Tang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9377-9386

Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies

Shengpu Tang, Aditya Modi, Michael Sjoding, Jenna Wiens; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9387-9396

Taylor Expansion Policy Optimization

Yunhao Tang, Michal Valko, Remi Munos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9397-9406

Variational Imitation Learning with Diverse-quality Demonstrations

Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9407-9417

Learning disconnected manifolds: a no GAN’s land

Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, Jeremie Mary; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9418-9427

No-Regret Exploration in Goal-Oriented Reinforcement Learning

Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9428-9437

Sparse Sinkhorn Attention

Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, Da-Cheng Juan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9438-9447

Inductive Relation Prediction by Subgraph Reasoning

Komal Teru, Etienne Denis, Will Hamilton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9448-9457

Few-shot Domain Adaptation by Causal Mechanism Transfer

Takeshi Teshima, Issei Sato, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9458-9469

Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension

Yuandong Tian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9470-9480

Sequential Transfer in Reinforcement Learning with a Generative Model

Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9481-9492

Convolutional dictionary learning based auto-encoders for natural exponential-family distributions

Bahareh Tolooshams, Andrew Song, Simona Temereanca, Demba Ba; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9493-9503

Multi-step Greedy Reinforcement Learning Algorithms

Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9504-9513

Choice Set Optimization Under Discrete Choice Models of Group Decisions

Kiran Tomlinson, Austin Benson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9514-9525

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

Alexander Tong, Jessie Huang, Guy Wolf, David Van Dijk, Smita Krishnaswamy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9526-9536

Alleviating Privacy Attacks via Causal Learning

Shruti Tople, Amit Sharma, Aditya Nori; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9537-9547

Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances

Csaba Toth, Harald Oberhauser; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9548-9560

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations

Florian Tramer, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Joern-Henrik Jacobsen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9561-9571

Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization

Quoc Tran-Dinh, Nhan Pham, Lam Nguyen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9572-9582

Bayesian Differential Privacy for Machine Learning

Aleksei Triastcyn, Boi Faltings; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9583-9592

Single Point Transductive Prediction

Nilesh Tripuraneni, Lester Mackey; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9593-9602

GraphOpt: Learning Optimization Models of Graph Formation

Rakshit Trivedi, Jiachen Yang, Hongyuan Zha; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9603-9613

Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources

Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9614-9624

From ImageNet to Image Classification: Contextualizing Progress on Benchmarks

Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9625-9635

Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis

Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9636-9647

Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network

Javier Turek, Shailee Jain, Vy Vo, Mihai Capotă, Alexander Huth, Theodore Willke; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9648-9658

Minimax Weight and Q-Function Learning for Off-Policy Evaluation

Masatoshi Uehara, Jiawei Huang, Nan Jiang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9659-9668

StochasticRank: Global Optimization of Scale-Free Discrete Functions

Aleksei Ustimenko, Liudmila Prokhorenkova; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9669-9679

Undirected Graphical Models as Approximate Posteriors

Arash Vahdat, Evgeny Andriyash, William Macready; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9680-9689

Uncertainty Estimation Using a Single Deep Deterministic Neural Network

Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9690-9700

Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks

Marko Vasic, Cameron Chalk, Sarfraz Khurshid, David Soloveichik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9701-9711

Linear bandits with Stochastic Delayed Feedback

Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis, Michael Brückner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9712-9721

Non-Stationary Delayed Bandits with Intermediate Observations

Claire Vernade, Andras Gyorgy, Timothy Mann; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9722-9732

OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning

Alexander Vezhnevets, Yuhuai Wu, Maria Eckstein, Rémi Leblond, Joel Z Leibo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9733-9742

Born-Again Tree Ensembles

Thibaut Vidal, Maximilian Schiffer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9743-9753

Private Reinforcement Learning with PAC and Regret Guarantees

Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9754-9764

New Oracle-Efficient Algorithms for Private Synthetic Data Release

Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Steven Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9765-9774

Conditional gradient methods for stochastically constrained convex minimization

Maria-Luiza Vladarean, Ahmet Alacaoglu, Ya-Ping Hsieh, Volkan Cevher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9775-9785

Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

Andrey Voynov, Artem Babenko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9786-9796

Safe Reinforcement Learning in Constrained Markov Decision Processes

Akifumi Wachi, Yanan Sui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9797-9806

Orthogonalized SGD and Nested Architectures for Anytime Neural Networks

Chengcheng Wan, Henry Hoffmann, Shan Lu, Michael Maire; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9807-9817

Projection-free Distributed Online Convex Optimization with $O(\sqrtT)$ Communication Complexity

Yuanyu Wan, Wei-Wei Tu, Lijun Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9818-9828

Logistic Regression for Massive Data with Rare Events

Haiying Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9829-9836

On the Global Optimality of Model-Agnostic Meta-Learning

Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9837-9846

Towards Accurate Post-training Network Quantization via Bit-Split and Stitching

Peisong Wang, Qiang Chen, Xiangyu He, Jian Cheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9847-9856

Self-Modulating Nonparametric Event-Tensor Factorization

Zheng Wang, Xinqi Chu, Shandian Zhe; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9857-9867

Upper bounds for Model-Free Row-Sparse Principal Component Analysis

Guanyi Wang, Santanu Dey; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9868-9875

ROMA: Multi-Agent Reinforcement Learning with Emergent Roles

Tonghan Wang, Heng Dong, Victor Lesser, Chongjie Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9876-9886

Non-separable Non-stationary random fields

Kangrui Wang, Oliver Hamelijnck, Theodoros Damoulas, Mark Steel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9887-9897

Continuously Indexed Domain Adaptation

Hao Wang, Hao He, Dina Katabi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9898-9907

Learning Efficient Multi-agent Communication: An Information Bottleneck Approach

Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, Zinovi Rabinovich; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9908-9918

Frustratingly Simple Few-Shot Object Detection

Xin Wang, Thomas Huang, Joseph Gonzalez, Trevor Darrell, Fisher Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9919-9928

Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

Tongzhou Wang, Phillip Isola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9929-9939

Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, Kenneth Stanley; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9940-9951

Haar Graph Pooling

Yu Guang Wang, Ming Li, Zheng Ma, Guido Montufar, Xiaosheng Zhuang, Yanan Fan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9952-9962

Deep Streaming Label Learning

Zhen Wang, Liu Liu, Dacheng Tao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9963-9972

BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates

Xiaochen Wang, Arash Pakbin, Bobak Mortazavi, Hongyu Zhao, Donald Lee; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9973-9982

Optimizing Data Usage via Differentiable Rewards

Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell, Graham Neubig; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9983-9995

Bandits for BMO Functions

Tianyu Wang, Cynthia Rudin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9996-10006

When deep denoising meets iterative phase retrieval

Yaotian Wang, Xiaohang Sun, Jason Fleischer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10007-10017

Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables

Qi Wang, Herke Van Hoof; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10018-10028

Loss Function Search for Face Recognition

Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, Tao Mei; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10029-10038

Sequential Cooperative Bayesian Inference

Junqi Wang, Pei Wang, Patrick Shafto; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10039-10049

Neural Network Control Policy Verification With Persistent Adversarial Perturbation

Yuh-Shyang Wang, Lily Weng, Luca Daniel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10050-10059

Cost-effectively Identifying Causal Effects When Only Response Variable is Observable

Tian-Zuo Wang, Xi-Zhu Wu, Sheng-Jun Huang, Zhi-Hua Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10060-10069

Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling

Che Wang, Yanqiu Wu, Quan Vuong, Keith Ross; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10070-10080

On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data

Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10081-10091

Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning

Lingxiao Wang, Zhuoran Yang, Zhaoran Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10092-10103

On Lp-norm Robustness of Ensemble Decision Stumps and Trees

Yihan Wang, Huan Zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10104-10114

Thompson Sampling via Local Uncertainty

Zhendong Wang, Mingyuan Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10115-10125

A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model

Peng Wang, Zirui Zhou, Anthony Man-Cho So; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10126-10135

Learning Representations that Support Extrapolation

Taylor Webb, Zachary Dulberg, Steven Frankland, Alexander Petrov, Randall O’Reilly, Jonathan Cohen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10136-10146

Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems

Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Schönlieb, Hua Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10158-10169

Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes

Chen-Yu Wei, Mehdi Jafarnia Jahromi, Haipeng Luo, Hiteshi Sharma, Rahul Jain; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10170-10180

The Implicit and Explicit Regularization Effects of Dropout

Colin Wei, Sham Kakade, Tengyu Ma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10181-10192

Online Control of the False Coverage Rate and False Sign Rate

Asaf Weinstein, Aaditya Ramdas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10193-10202

Batch Stationary Distribution Estimation

Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10203-10213

Domain Aggregation Networks for Multi-Source Domain Adaptation

Junfeng Wen, Russell Greiner, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10214-10224

Towards Understanding the Regularization of Adversarial Robustness on Neural Networks

Yuxin Wen, Shuai Li, Kui Jia; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10225-10235

Amortised Learning by Wake-Sleep

Li Wenliang, Theodore Moskovitz, Heishiro Kanagawa, Maneesh Sahani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10236-10247

How Good is the Bayes Posterior in Deep Neural Networks Really?

Florian Wenzel, Kevin Roth, Bastiaan Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10248-10259

Predictive Sampling with Forecasting Autoregressive Models

Auke Wiggers, Emiel Hoogeboom; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10260-10269

State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes

William Wilkinson, Paul Chang, Michael Andersen, Arno Solin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10270-10281

Efficient nonparametric statistical inference on population feature importance using Shapley values

Brian Williamson, Jean Feng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10282-10291

Efficiently sampling functions from Gaussian process posteriors

James Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10292-10302

Learning to Rank Learning Curves

Martin Wistuba, Tejaswini Pedapati; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10303-10312

Causal Inference using Gaussian Processes with Structured Latent Confounders

Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10313-10323

Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling

David Woodruff, Amir Zandieh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10324-10333

Is Local SGD Better than Minibatch SGD?

Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan Mcmahan, Ohad Shamir, Nathan Srebro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10334-10343

Obtaining Adjustable Regularization for Free via Iterate Averaging

Jingfeng Wu, Vladimir Braverman, Lin Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10344-10354

DeltaGrad: Rapid retraining of machine learning models

Yinjun Wu, Edgar Dobriban, Susan Davidson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10355-10366

On the Noisy Gradient Descent that Generalizes as SGD

Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10367-10376

Stronger and Faster Wasserstein Adversarial Attacks

Kaiwen Wu, Allen Wang, Yaoliang Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10377-10387

Sequence Generation with Mixed Representations

Lijun Wu, Shufang Xie, Yingce Xia, Yang Fan, Jian-Huang Lai, Tao Qin, Tieyan Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10388-10398

Adversarial Robustness via Runtime Masking and Cleansing

Yi-Hsuan Wu, Chia-Hung Yuan, Shan-Hung Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10399-10409

On the Generalization Effects of Linear Transformations in Data Augmentation

Sen Wu, Hongyang Zhang, Gregory Valiant, Christopher Re; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10410-10420

Amortized Population Gibbs Samplers with Neural Sufficient Statistics

Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem Van De Meent; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10421-10431

Continuous Graph Neural Networks

Louis-Pascal Xhonneux, Meng Qu, Jian Tang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10432-10441

A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning

Yunhua Xiang, Noah Simon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10442-10451

Generative Flows with Matrix Exponential

Changyi Xiao, Ligang Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10452-10461

Disentangling Trainability and Generalization in Deep Neural Networks

Lechao Xiao, Jeffrey Pennington, Samuel Schoenholz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10462-10472

Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing

Yuxuan Xie, Jilles Dibangoye, Olivier Buffet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10473-10482

Maximum-and-Concatenation Networks

Xingyu Xie, Hao Kong, Jianlong Wu, Wayne Zhang, Guangcan Liu, Zhouchen Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10483-10494

Zeno++: Robust Fully Asynchronous SGD

Cong Xie, Sanmi Koyejo, Indranil Gupta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10495-10503

Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems

Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10504-10513

On the Number of Linear Regions of Convolutional Neural Networks

Huan Xiong, Lei Huang, Mengyang Yu, Li Liu, Fan Zhu, Ling Shao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10514-10523

On Layer Normalization in the Transformer Architecture

Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tieyan Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10524-10533

On Variational Learning of Controllable Representations for Text without Supervision

Peng Xu, Jackie Chi Kit Cheung, Yanshuai Cao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10534-10543

Class-Weighted Classification: Trade-offs and Robust Approaches

Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10544-10554

A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation

Pan Xu, Quanquan Gu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10555-10565

Understanding and Stabilizing GANs’ Training Dynamics Using Control Theory

Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10566-10575

Learning Autoencoders with Relational Regularization

Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, Lawrence Carin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10576-10586

Learning Factorized Weight Matrix for Joint Filtering

Xiangyu Xu, Yongrui Ma, Wenxiu Sun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10587-10596

Variational Label Enhancement

Ning Xu, Jun Shu, Yun-Peng Liu, Xin Geng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10597-10606

Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, Wojciech Matusik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10607-10616

MetaFun: Meta-Learning with Iterative Functional Updates

Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam Kosiorek, Yee Whye Teh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10617-10627

Video Prediction via Example Guidance

Jingwei Xu, Huazhe Xu, Bingbing Ni, Xiaokang Yang, Trevor Darrell; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10628-10637

Amortized Finite Element Analysis for Fast PDE-Constrained Optimization

Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan Adams; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10638-10647

Feature Selection using Stochastic Gates

Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10648-10659

Stochastic Optimization for Non-convex Inf-Projection Problems

Yan Yan, Yi Xu, Lijun Zhang, Wang Xiaoyu, Tianbao Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10660-10669

Variational Bayesian Quantization

Yibo Yang, Robert Bamler, Stephan Mandt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10670-10680

Energy-Based Processes for Exchangeable Data

Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10681-10692

Randomized Smoothing of All Shapes and Sizes

Greg Yang, Tony Duan, J. Edward Hu, Hadi Salman, Ilya Razenshteyn, Jerry Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10693-10705

Q-value Path Decomposition for Deep Multiagent Reinforcement Learning

Yaodong Yang, Jianye Hao, Guangyong Chen, Hongyao Tang, Yingfeng Chen, Yujing Hu, Changjie Fan, Zhongyu Wei; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10706-10715

Improving Molecular Design by Stochastic Iterative Target Augmentation

Kevin Yang, Wengong Jin, Kyle Swanson, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10716-10726

On the consistency of top-k surrogate losses

Forest Yang, Sanmi Koyejo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10727-10735

Interpolation between Residual and Non-Residual Networks

Zonghan Yang, Yang Liu, Chenglong Bao, Zuoqiang Shi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10736-10745

Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound

Lin Yang, Mengdi Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10746-10756

Multi-Agent Determinantal Q-Learning

Yaodong Yang, Ying Wen, Jun Wang, Liheng Chen, Kun Shao, David Mguni, Weinan Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10757-10766

Rethinking Bias-Variance Trade-off for Generalization of Neural Networks

Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10767-10777

Unsupervised Transfer Learning for Spatiotemporal Predictive Networks

Zhiyu Yao, Yunbo Wang, Mingsheng Long, Jianmin Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10778-10788

Searching to Exploit Memorization Effect in Learning with Noisy Labels

Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10789-10798

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

Michihiro Yasunaga, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10799-10808

Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification

Hui Ye, Zhiyu Chen, Da-Han Wang, Brian Davison; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10809-10819

Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection

Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10820-10830

It’s Not What Machines Can Learn, It’s What We Cannot Teach

Gal Yehuda, Moshe Gabel, Assaf Schuster; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10831-10841

Data Valuation using Reinforcement Learning

Jinsung Yoon, Sercan Arik, Tomas Pfister; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10842-10851

XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning

Sung Whan Yoon, Do-Yeon Kim, Jun Seo, Jaekyun Moon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10852-10860

Robustifying Sequential Neural Processes

Jaesik Yoon, Gautam Singh, Sungjin Ahn; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10861-10870

When Does Self-Supervision Help Graph Convolutional Networks?

Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10871-10880

Graph Structure of Neural Networks

Jiaxuan You, Jure Leskovec, Kaiming He, Saining Xie; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10881-10891

Simultaneous Inference for Massive Data: Distributed Bootstrap

Yang Yu, Shih-Kang Chao, Guang Cheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10892-10901

Graphical Models Meet Bandits: A Variational Thompson Sampling Approach

Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10902-10912

Label-Noise Robust Domain Adaptation

Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10913-10924

Intrinsic Reward Driven Imitation Learning via Generative Model

Xingrui Yu, Yueming Lyu, Ivor Tsang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10925-10935

Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

Wenhui Yu, Zheng Qin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10936-10945

Federated Learning with Only Positive Labels

Felix Yu, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10946-10956

Training Deep Energy-Based Models with f-Divergence Minimization

Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10957-10967

Graph Random Neural Features for Distance-Preserving Graph Representations

Daniele Zambon, Cesare Alippi, Lorenzo Livi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10968-10977

Learning Near Optimal Policies with Low Inherent Bellman Error

Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, Emma Brunskill; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10978-10989

Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing

Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van Den Broeck; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10990-11000

Learning Calibratable Policies using Programmatic Style-Consistency

Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11001-11011

Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach

Junzhe Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11012-11022

Robustness to Programmable String Transformations via Augmented Abstract Training

Yuhao Zhang, Aws Albarghouthi, Loris D’Antoni; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11023-11032

Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games

Youzhi Zhang, Bo An; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11033-11043

Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate

Yufeng Zhang, Qi Cai, Zhuoran Yang, Zhaoran Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11044-11054

Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings

Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11055-11065

Learning the Valuations of a $k$-demand Agent

Hanrui Zhang, Vincent Conitzer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11066-11075

A Tree-Structured Decoder for Image-to-Markup Generation

Jianshu Zhang, Jun Du, Yongxin Yang, Yi-Zhe Song, Si Wei, Lirong Dai; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11076-11085

Approximation Capabilities of Neural ODEs and Invertible Residual Networks

Han Zhang, Xi Gao, Jacob Unterman, Tom Arodz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11086-11095

Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization

Richard Zhang, Daniel Golovin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11096-11105

Spread Divergence

Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11106-11116

Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning

Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11117-11128

Privately Learning Markov Random Fields

Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Steven Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11129-11140

Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective

Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11141-11152

Optimal Estimator for Unlabeled Linear Regression

Hang Zhang, Ping Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11153-11162

Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks

Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11163-11172

Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions

Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11173-11182

Self-Attentive Hawkes Process

Qiang Zhang, Aldo Lipani, Omer Kirnap, Emine Yilmaz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11183-11193

GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values

Shangtong Zhang, Bo Liu, Shimon Whiteson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11194-11203

Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation

Shangtong Zhang, Bo Liu, Hengshuai Yao, Shimon Whiteson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11204-11213

Invariant Causal Prediction for Block MDPs

Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11214-11224

Adaptive Reward-Poisoning Attacks against Reinforcement Learning