Volume 97: International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA

[edit]

Editors: Kamalika Chaudhuri, Ruslan Salakhutdinov

[bib][citeproc]

AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs

Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf, Stefan Bauer ; PMLR 97:1-10

Dynamic Weights in Multi-Objective Deep Reinforcement Learning

Axel Abels, Diederik Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher ; PMLR 97:11-20

MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing

Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan ; PMLR 97:21-29

Communication-Constrained Inference and the Role of Shared Randomness

Jayadev Acharya, Clement Canonne, Himanshu Tyagi ; PMLR 97:30-39

Distributed Learning with Sublinear Communication

Jayadev Acharya, Chris De Sa, Dylan Foster, Karthik Sridharan ; PMLR 97:40-50

Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters

Jayadev Acharya, Ziteng Sun ; PMLR 97:51-60

Learning Models from Data with Measurement Error: Tackling Underreporting

Roy Adams, Yuelong Ji, Xiaobin Wang, Suchi Saria ; PMLR 97:61-70

TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning

Tameem Adel, Adrian Weller ; PMLR 97:71-81

PAC Learnability of Node Functions in Networked Dynamical Systems

Abhijin Adiga, Chris J Kuhlman, Madhav Marathe, S Ravi, Anil Vullikanti ; PMLR 97:82-91

Static Automatic Batching In TensorFlow

Ashish Agarwal ; PMLR 97:92-101

Efficient Full-Matrix Adaptive Regularization

Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang ; PMLR 97:102-110

Online Control with Adversarial Disturbances

Naman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, Karan Singh ; PMLR 97:111-119

Fair Regression: Quantitative Definitions and Reduction-Based Algorithms

Alekh Agarwal, Miroslav Dudik, Zhiwei Steven Wu ; PMLR 97:120-129

Learning to Generalize from Sparse and Underspecified Rewards

Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi ; PMLR 97:130-140

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions

Raj Agrawal, Brian Trippe, Jonathan Huggins, Tamara Broderick ; PMLR 97:141-150

Understanding the Impact of Entropy on Policy Optimization

Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans ; PMLR 97:151-160

Fairwashing: the risk of rationalization

Ulrich Aivodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp ; PMLR 97:161-170

Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search

Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishida ; PMLR 97:171-180

Projections for Approximate Policy Iteration Algorithms

Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann ; PMLR 97:181-190

Validating Causal Inference Models via Influence Functions

Ahmed Alaa, Mihaela Van Der Schaar ; PMLR 97:191-201

Multi-objective training of Generative Adversarial Networks with multiple discriminators

Isabela Albuquerque, Joao Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas ; PMLR 97:202-211

Graph Element Networks: adaptive, structured computation and memory

Ferran Alet, Adarsh Keshav Jeewajee, Maria Bauza Villalonga, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Kaelbling ; PMLR 97:212-222

Analogies Explained: Towards Understanding Word Embeddings

Carl Allen, Timothy Hospedales ; PMLR 97:223-231

Infinite Mixture Prototypes for Few-shot Learning

Kelsey Allen, Evan Shelhamer, Hanul Shin, Joshua Tenenbaum ; PMLR 97:232-241

A Convergence Theory for Deep Learning via Over-Parameterization

Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song ; PMLR 97:242-252

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

Ahsan Alvi, Binxin Ru, Jan-Peter Calliess, Stephen Roberts, Michael A. Osborne ; PMLR 97:253-262

Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy

Kareem Amin, Alex Kulesza, Andres Munoz, Sergei Vassilvtiskii ; PMLR 97:263-271

Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation

Marco Ancona, Cengiz Oztireli, Markus Gross ; PMLR 97:272-281

Scaling Up Ordinal Embedding: A Landmark Approach

Jesse Anderton, Javed Aslam ; PMLR 97:282-290

Sorting Out Lipschitz Function Approximation

Cem Anil, James Lucas, Roger Grosse ; PMLR 97:291-301

Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data

Luigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi ; PMLR 97:302-311

Unsupervised Label Noise Modeling and Loss Correction

Eric Arazo, Diego Ortego, Paul Albert, Noel O’Connor, Kevin Mcguinness ; PMLR 97:312-321

Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks

Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, Ruosong Wang ; PMLR 97:322-332

Distributed Weighted Matching via Randomized Composable Coresets

Sepehr Assadi, Mohammadhossein Bateni, Vahab Mirrokni ; PMLR 97:333-343

Stochastic Gradient Push for Distributed Deep Learning

Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Mike Rabbat ; PMLR 97:344-353

Bayesian Optimization of Composite Functions

Raul Astudillo, Peter Frazier ; PMLR 97:354-363

Linear-Complexity Data-Parallel Earth Mover’s Distance Approximations

Kubilay Atasu, Thomas Mittelholzer ; PMLR 97:364-373

Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA

Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra Slavković ; PMLR 97:374-384

Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data

Sergul Aydore, Bertrand Thirion, Gael Varoquaux ; PMLR 97:385-394

Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior

Fadhel Ayed, Juho Lee, Francois Caron ; PMLR 97:395-404

Scalable Fair Clustering

Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner ; PMLR 97:405-413

Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs

Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi ; PMLR 97:414-423

Provable Guarantees for Gradient-Based Meta-Learning

Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar ; PMLR 97:424-433

Open-ended learning in symmetric zero-sum games

David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel ; PMLR 97:434-443

Concrete Autoencoders: Differentiable Feature Selection and Reconstruction

Muhammed Fatih Balın, Abubakar Abid, James Zou ; PMLR 97:444-453

HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving

Kshitij Bansal, Sarah Loos, Markus Rabe, Christian Szegedy, Stewart Wilcox ; PMLR 97:454-463

Structured agents for physical construction

Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly Stachenfeld, Pushmeet Kohli, Peter Battaglia, Jessica Hamrick ; PMLR 97:464-474

Learning to Route in Similarity Graphs

Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenko ; PMLR 97:475-484

A Personalized Affective Memory Model for Improving Emotion Recognition

Pablo Barros, German Parisi, Stefan Wermter ; PMLR 97:485-494

Scale-free adaptive planning for deterministic dynamics & discounted rewards

Peter Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko ; PMLR 97:495-504

Pareto Optimal Streaming Unsupervised Classification

Soumya Basu, Steven Gutstein, Brent Lance, Sanjay Shakkottai ; PMLR 97:505-514

Categorical Feature Compression via Submodular Optimization

Mohammadhossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab Mirrokni, Afshin Rostamizadeh ; PMLR 97:515-523

Noise2Self: Blind Denoising by Self-Supervision

Joshua Batson, Loic Royer ; PMLR 97:524-533

Efficient optimization of loops and limits with randomized telescoping sums

Alex Beatson, Ryan P Adams ; PMLR 97:534-543

Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces

Philipp Becker, Harit Pandya, Gregor Gebhardt, Cheng Zhao, C. James Taylor, Gerhard Neumann ; PMLR 97:544-552

Switching Linear Dynamics for Variational Bayes Filtering

Philip Becker-Ehmck, Jan Peters, Patrick Van Der Smagt ; PMLR 97:553-562

Active Learning for Probabilistic Structured Prediction of Cuts and Matchings

Sima Behpour, Anqi Liu, Brian Ziebart ; PMLR 97:563-572

Invertible Residual Networks

Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen ; PMLR 97:573-582

Greedy Layerwise Learning Can Scale To ImageNet

Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon ; PMLR 97:583-593

Overcoming Multi-model Forgetting

Yassine Benyahia, Kaicheng Yu, Kamil Bennani Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musat ; PMLR 97:594-603

Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning

Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger ; PMLR 97:604-613

Adversarially Learned Representations for Information Obfuscation and Inference

Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Galen Reeves, Guillermo Sapiro ; PMLR 97:614-623

Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case

Alina Beygelzimer, David Pal, Balazs Szorenyi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang ; PMLR 97:624-633

Analyzing Federated Learning through an Adversarial Lens

Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin Calo ; PMLR 97:634-643

Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference

Yatao Bian, Joachim Buhmann, Andreas Krause ; PMLR 97:644-653

More Efficient Off-Policy Evaluation through Regularized Targeted Learning

Aurelien Bibaut, Ivana Malenica, Nikos Vlassis, Mark Van Der Laan ; PMLR 97:654-663

A Kernel Perspective for Regularizing Deep Neural Networks

Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal ; PMLR 97:664-674

Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff

Yochai Blau, Tomer Michaeli ; PMLR 97:675-685

Correlated bandits or: How to minimize mean-squared error online

Vinay Praneeth Boda, Prashanth L.A. ; PMLR 97:686-694

Adversarial Attacks on Node Embeddings via Graph Poisoning

Aleksandar Bojchevski, Stephan Günnemann ; PMLR 97:695-704

Online Variance Reduction with Mixtures

Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause ; PMLR 97:705-714

Compositional Fairness Constraints for Graph Embeddings

Avishek Bose, William Hamilton ; PMLR 97:715-724

Unreproducible Research is Reproducible

Xavier Bouthillier, César Laurent, Pascal Vincent ; PMLR 97:725-734

Blended Conditonal Gradients

Gábor Braun, Sebastian Pokutta, Dan Tu, Stephen Wright ; PMLR 97:735-743

Coresets for Ordered Weighted Clustering

Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu ; PMLR 97:744-753

Target Tracking for Contextual Bandits: Application to Demand Side Management

Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz ; PMLR 97:754-763

Active Manifolds: A non-linear analogue to Active Subspaces

Robert Bridges, Anthony Gruber, Christopher Felder, Miki Verma, Chelsey Hoff ; PMLR 97:764-772

Conditioning by adaptive sampling for robust design

David Brookes, Hahnbeom Park, Jennifer Listgarten ; PMLR 97:773-782

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations

Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum ; PMLR 97:783-792

Deep Counterfactual Regret Minimization

Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm ; PMLR 97:793-802

Understanding the Origins of Bias in Word Embeddings

Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel ; PMLR 97:803-811

Low Latency Privacy Preserving Inference

Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha ; PMLR 97:812-821

Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem

Alon Brutzkus, Amir Globerson ; PMLR 97:822-830

Adversarial examples from computational constraints

Sebastien Bubeck, Yin Tat Lee, Eric Price, Ilya Razenshteyn ; PMLR 97:831-840

Self-similar Epochs: Value in arrangement

Eliav Buchnik, Edith Cohen, Avinatan Hasidim, Yossi Matias ; PMLR 97:841-850

Learning Generative Models across Incomparable Spaces

Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka ; PMLR 97:851-861

Rates of Convergence for Sparse Variational Gaussian Process Regression

David Burt, Carl Edward Rasmussen, Mark Van Der Wilk ; PMLR 97:862-871

What is the Effect of Importance Weighting in Deep Learning?

Jonathon Byrd, Zachary Lipton ; PMLR 97:872-881

A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent

Yongqiang Cai, Qianxiao Li, Zuowei Shen ; PMLR 97:882-890

Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances

Bugra Can, Mert Gurbuzbalaban, Lingjiong Zhu ; PMLR 97:891-901

Active Embedding Search via Noisy Paired Comparisons

Gregory Canal, Andy Massimino, Mark Davenport, Christopher Rozell ; PMLR 97:902-911

Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem

Junyu Cao, Wei Sun ; PMLR 97:912-920

Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games

Adrian Rivera Cardoso, Jacob Abernethy, He Wang, Huan Xu ; PMLR 97:921-930

Automated Model Selection with Bayesian Quadrature

Henry Chai, Jean-Francois Ton, Michael A. Osborne, Roman Garnett ; PMLR 97:931-940

Learning Action Representations for Reinforcement Learning

Yash Chandak, Georgios Theocharous, James Kostas, Scott Jordan, Philip Thomas ; PMLR 97:941-950

Dynamic Measurement Scheduling for Event Forecasting using Deep RL

Chun-Hao Chang, Mingjie Mai, Anna Goldenberg ; PMLR 97:951-960

On Symmetric Losses for Learning from Corrupted Labels

Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama ; PMLR 97:961-970

Online learning with kernel losses

Niladri Chatterji, Aldo Pacchiano, Peter Bartlett ; PMLR 97:971-980

Neural Network Attributions: A Causal Perspective

Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N Balasubramanian ; PMLR 97:981-990

PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits

Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan ; PMLR 97:991-1000

Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates

George Chen ; PMLR 97:1001-1010

Stein Point Markov Chain Monte Carlo

Wilson Ye Chen, Alessandro Barp, Francois-Xavier Briol, Jackson Gorham, Mark Girolami, Lester Mackey, Chris Oates ; PMLR 97:1011-1021

Particle Flow Bayes’ Rule

Xinshi Chen, Hanjun Dai, Le Song ; PMLR 97:1022-1031

Proportionally Fair Clustering

Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala ; PMLR 97:1032-1041

Information-Theoretic Considerations in Batch Reinforcement Learning

Jinglin Chen, Nan Jiang ; PMLR 97:1042-1051

Generative Adversarial User Model for Reinforcement Learning Based Recommendation System

Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song ; PMLR 97:1052-1061

Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

Pengfei Chen, Ben Ben Liao, Guangyong Chen, Shengyu Zhang ; PMLR 97:1062-1070

A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization

Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng ; PMLR 97:1071-1080

Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation

Xinyang Chen, Sinan Wang, Mingsheng Long, Jianmin Wang ; PMLR 97:1081-1090

Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications

Pin-Yu Chen, Lingfei Wu, Sijia Liu, Indika Rajapakse ; PMLR 97:1091-1101

Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number

Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang ; PMLR 97:1102-1111

Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching

Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Lin ; PMLR 97:1112-1121

Robust Decision Trees Against Adversarial Examples

Hongge Chen, Huan Zhang, Duane Boning, Cho-Jui Hsieh ; PMLR 97:1122-1131

RaFM: Rank-Aware Factorization Machines

Xiaoshuang Chen, Yin Zheng, Jiaxing Wang, Wenye Ma, Junzhou Huang ; PMLR 97:1132-1140

Control Regularization for Reduced Variance Reinforcement Learning

Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick ; PMLR 97:1141-1150

Predictor-Corrector Policy Optimization

Ching-An Cheng, Xinyan Yan, Nathan Ratliff, Byron Boots ; PMLR 97:1151-1161

Variational Inference for sparse network reconstruction from count data

Julien Chiquet, Stephane Robin, Mahendra Mariadassou ; PMLR 97:1162-1171

Random Walks on Hypergraphs with Edge-Dependent Vertex Weights

Uthsav Chitra, Benjamin Raphael ; PMLR 97:1172-1181

Neural Joint Source-Channel Coding

Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon ; PMLR 97:1182-1192

Beyond Backprop: Online Alternating Minimization with Auxiliary Variables

Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf ; PMLR 97:1193-1202

Unifying Orthogonal Monte Carlo Methods

Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller ; PMLR 97:1203-1212

Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning

Casey Chu, Jose Blanchet, Peter Glynn ; PMLR 97:1213-1222

MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization

Eric Chu, Peter Liu ; PMLR 97:1223-1232

Weak Detection of Signal in the Spiked Wigner Model

Hye Won Chung, Ji Oon Lee ; PMLR 97:1233-1241

New results on information theoretic clustering

Ferdinando Cicalese, Eduardo Laber, Lucas Murtinho ; PMLR 97:1242-1251

Sensitivity Analysis of Linear Structural Causal Models

Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim ; PMLR 97:1252-1261

Dimensionality Reduction for Tukey Regression

Kenneth Clarkson, Ruosong Wang, David Woodruff ; PMLR 97:1262-1271

On Medians of (Randomized) Pairwise Means

Pierre Laforgue, Stephan Clemencon, Patrice Bertail ; PMLR 97:1272-1281

Quantifying Generalization in Reinforcement Learning

Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman ; PMLR 97:1282-1289

Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models

Eldan Cohen, Christopher Beck ; PMLR 97:1290-1299

Learning Linear-Quadratic Regulators Efficiently with only $\sqrtT$ Regret

Alon Cohen, Tomer Koren, Yishay Mansour ; PMLR 97:1300-1309

Certified Adversarial Robustness via Randomized Smoothing

Jeremy Cohen, Elan Rosenfeld, Zico Kolter ; PMLR 97:1310-1320

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling ; PMLR 97:1321-1330

CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning

Cédric Colas, Pierre Fournier, Mohamed Chetouani, Olivier Sigaud, Pierre-Yves Oudeyer ; PMLR 97:1331-1340

A fully differentiable beam search decoder

Ronan Collobert, Awni Hannun, Gabriel Synnaeve ; PMLR 97:1341-1350

Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets

Rob Cornish, Paul Vanetti, Alexandre Bouchard-Cote, George Deligiannidis, Arnaud Doucet ; PMLR 97:1351-1360

Adjustment Criteria for Generalizing Experimental Findings

Juan Correa, Jin Tian, Elias Bareinboim ; PMLR 97:1361-1369

Online Learning with Sleeping Experts and Feedback Graphs

Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Scott Yang ; PMLR 97:1370-1378

Active Learning with Disagreement Graphs

Corinna Cortes, Giulia Desalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile ; PMLR 97:1379-1387

Shape Constraints for Set Functions

Andrew Cotter, Maya Gupta, Heinrich Jiang, Erez Louidor, James Muller, Tamann Narayan, Serena Wang, Tao Zhu ; PMLR 97:1388-1396

Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints

Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You ; PMLR 97:1397-1405

Monge blunts Bayes: Hardness Results for Adversarial Training

Zac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder ; PMLR 97:1406-1415

Boosted Density Estimation Remastered

Zac Cranko, Richard Nock ; PMLR 97:1416-1425

Submodular Cost Submodular Cover with an Approximate Oracle

Victoria Crawford, Alan Kuhnle, My Thai ; PMLR 97:1426-1435

Flexibly Fair Representation Learning by Disentanglement

Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel ; PMLR 97:1436-1445

Anytime Online-to-Batch, Optimism and Acceleration

Ashok Cutkosky ; PMLR 97:1446-1454

Matrix-Free Preconditioning in Online Learning

Ashok Cutkosky, Tamas Sarlos ; PMLR 97:1455-1464

Minimal Achievable Sufficient Statistic Learning

Milan Cvitkovic, Günther Koliander ; PMLR 97:1465-1474

Open Vocabulary Learning on Source Code with a Graph-Structured Cache

Milan Cvitkovic, Badal Singh, Animashree Anandkumar ; PMLR 97:1475-1485

The Value Function Polytope in Reinforcement Learning

Robert Dadashi, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare ; PMLR 97:1486-1495

Bayesian Optimization Meets Bayesian Optimal Stopping

Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet ; PMLR 97:1496-1506

Policy Certificates: Towards Accountable Reinforcement Learning

Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill ; PMLR 97:1507-1516

Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations

Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Re ; PMLR 97:1517-1527

A Kernel Theory of Modern Data Augmentation

Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Re ; PMLR 97:1528-1537

TarMAC: Targeted Multi-Agent Communication

Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau ; PMLR 97:1538-1546

Teaching a black-box learner

Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu ; PMLR 97:1547-1555

Stochastic Deep Networks

Gwendoline De Bie, Gabriel Peyré, Marco Cuturi ; PMLR 97:1556-1565

Learning-to-Learn Stochastic Gradient Descent with Biased Regularization

Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil ; PMLR 97:1566-1575

A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology

Onur Dereli, Ceyda Oğuz, Mehmet Gönen ; PMLR 97:1576-1585

Learning to Convolve: A Generalized Weight-Tying Approach

Nichita Diaconu, Daniel Worrall ; PMLR 97:1586-1595

Sever: A Robust Meta-Algorithm for Stochastic Optimization

Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart ; PMLR 97:1596-1606

Approximated Oracle Filter Pruning for Destructive CNN Width Optimization

Xiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, Chenggang Yan ; PMLR 97:1607-1616

Noisy Dual Principal Component Pursuit

Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Daniel Robinson, Manolis Tsakiris, Rene Vidal ; PMLR 97:1617-1625

Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning

Thinh Doan, Siva Maguluri, Justin Romberg ; PMLR 97:1626-1635

Trajectory-Based Off-Policy Deep Reinforcement Learning

Andreas Doerr, Michael Volpp, Marc Toussaint, Trimpe Sebastian, Christian Daniel ; PMLR 97:1636-1645

Generalized No Free Lunch Theorem for Adversarial Robustness

Elvis Dohmatob ; PMLR 97:1646-1654

Width Provably Matters in Optimization for Deep Linear Neural Networks

Simon Du, Wei Hu ; PMLR 97:1655-1664

Provably efficient RL with Rich Observations via Latent State Decoding

Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, John Langford ; PMLR 97:1665-1674

Gradient Descent Finds Global Minima of Deep Neural Networks

Simon Du, Jason Lee, Haochuan Li, Liwei Wang, Xiyu Zhai ; PMLR 97:1675-1685

Incorporating Grouping Information into Bayesian Decision Tree Ensembles

Junliang Du, Antonio Linero ; PMLR 97:1686-1695

Task-Agnostic Dynamics Priors for Deep Reinforcement Learning

Yilun Du, Karthic Narasimhan ; PMLR 97:1696-1705

Optimal Auctions through Deep Learning

Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David Parkes, Sai Srivatsa Ravindranath ; PMLR 97:1706-1715

Wasserstein of Wasserstein Loss for Learning Generative Models

Yonatan Dukler, Wuchen Li, Alex Lin, Guido Montufar ; PMLR 97:1716-1725

Learning interpretable continuous-time models of latent stochastic dynamical systems

Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani ; PMLR 97:1726-1734

Autoregressive Energy Machines

Conor Durkan, Charlie Nash ; PMLR 97:1735-1744

Band-limited Training and Inference for Convolutional Neural Networks

Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron Elmore, Michael Franklin ; PMLR 97:1745-1754

Imitating Latent Policies from Observation

Ashley Edwards, Himanshu Sahni, Yannick Schroecker, Charles Isbell ; PMLR 97:1755-1763

Semi-Cyclic Stochastic Gradient Descent

Hubert Eichner, Tomer Koren, Brendan Mcmahan, Nathan Srebro, Kunal Talwar ; PMLR 97:1764-1773

GDPP: Learning Diverse Generations using Determinantal Point Processes

Mohamed Elfeki, Camille Couprie, Morgane Riviere, Mohamed Elhoseiny ; PMLR 97:1774-1783

Sequential Facility Location: Approximate Submodularity and Greedy Algorithm

Ehsan Elhamifar ; PMLR 97:1784-1793

Improved Convergence for $\ell_1$ and $\ell_∞$ Regression via Iteratively Reweighted Least Squares

Alina Ene, Adrian Vladu ; PMLR 97:1794-1801

Exploring the Landscape of Spatial Robustness

Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry ; PMLR 97:1802-1811

Cross-Domain 3D Equivariant Image Embeddings

Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia ; PMLR 97:1812-1822

On the Connection Between Adversarial Robustness and Saliency Map Interpretability

Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schoenlieb ; PMLR 97:1823-1832

Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity

Matthew Fahrbach, Vahab Mirrokni, Morteza Zadimoghaddam ; PMLR 97:1833-1842

Multi-Frequency Vector Diffusion Maps

Yifeng Fan, Zhizhen Zhao ; PMLR 97:1843-1852

Stable-Predictive Optimistic Counterfactual Regret Minimization

Gabriele Farina, Christian Kroer, Noam Brown, Tuomas Sandholm ; PMLR 97:1853-1862

Regret Circuits: Composability of Regret Minimizers

Gabriele Farina, Christian Kroer, Tuomas Sandholm ; PMLR 97:1863-1872

Dead-ends and Secure Exploration in Reinforcement Learning

Mehdi Fatemi, Shikhar Sharma, Harm Van Seijen, Samira Ebrahimi Kahou ; PMLR 97:1873-1881

Invariant-Equivariant Representation Learning for Multi-Class Data

Ilya Feige ; PMLR 97:1882-1891

The advantages of multiple classes for reducing overfitting from test set reuse

Vitaly Feldman, Roy Frostig, Moritz Hardt ; PMLR 97:1892-1900

Decentralized Exploration in Multi-Armed Bandits

Raphael Feraud, Reda Alami, Romain Laroche ; PMLR 97:1901-1909

Almost surely constrained convex optimization

Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher ; PMLR 97:1910-1919

Online Meta-Learning

Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine ; PMLR 97:1920-1930

DL2: Training and Querying Neural Networks with Logic

Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin Vechev ; PMLR 97:1931-1941

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

Jakob Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling ; PMLR 97:1942-1951

Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap

Edwin Fong, Simon Lyddon, Chris Holmes ; PMLR 97:1952-1962

On discriminative learning of prediction uncertainty

Vojtech Franc, Daniel Prusa ; PMLR 97:1963-1971

Learning Discrete Structures for Graph Neural Networks

Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He ; PMLR 97:1972-1982

Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN

Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar ; PMLR 97:1983-1992

Approximating Orthogonal Matrices with Effective Givens Factorization

Thomas Frerix, Joan Bruna ; PMLR 97:1993-2001

Fast and Flexible Inference of Joint Distributions from their Marginals

Charlie Frogner, Tomaso Poggio ; PMLR 97:2002-2011

Analyzing and Improving Representations with the Soft Nearest Neighbor Loss

Nicholas Frosst, Nicolas Papernot, Geoffrey Hinton ; PMLR 97:2012-2020

Diagnosing Bottlenecks in Deep Q-learning Algorithms

Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine ; PMLR 97:2021-2030

MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement

Szu-Wei Fu, Chien-Feng Liao, Yu Tsao, Shou-De Lin ; PMLR 97:2031-2041

Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio

Kaito Fujii, Shinsaku Sakaue ; PMLR 97:2042-2051

Off-Policy Deep Reinforcement Learning without Exploration

Scott Fujimoto, David Meger, Doina Precup ; PMLR 97:2052-2062

Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

Shani Gamrian, Yoav Goldberg ; PMLR 97:2063-2072

Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities

Octavian Ganea, Sylvain Gelly, Gary Becigneul, Aliaksei Severyn ; PMLR 97:2073-2082

Graph U-Nets

Hongyang Gao, Shuiwang Ji ; PMLR 97:2083-2092

Deep Generative Learning via Variational Gradient Flow

Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, Shunkang Zhang ; PMLR 97:2093-2101

Rate Distortion For Model Compression:From Theory To Practice

Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh ; PMLR 97:2102-2111

Demystifying Dropout

Hongchang Gao, Jian Pei, Heng Huang ; PMLR 97:2112-2121

Geometric Scattering for Graph Data Analysis

Feng Gao, Guy Wolf, Matthew Hirn ; PMLR 97:2122-2131

Multi-Frequency Phase Synchronization

Tingran Gao, Zhizhen Zhao ; PMLR 97:2132-2141

Optimal Mini-Batch and Step Sizes for SAGA

Nidham Gazagnadou, Robert Gower, Joseph Salmon ; PMLR 97:2142-2150

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

Yonatan Geifman, Ran El-Yaniv ; PMLR 97:2151-2159

A Theory of Regularized Markov Decision Processes

Matthieu Geist, Bruno Scherrer, Olivier Pietquin ; PMLR 97:2160-2169

DeepMDP: Learning Continuous Latent Space Models for Representation Learning

Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare ; PMLR 97:2170-2179

Partially Linear Additive Gaussian Graphical Models

Sinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo ; PMLR 97:2180-2190

Learning and Data Selection in Big Datasets

Hossein Shokri Ghadikolaei, Hadi Ghauch, Carlo Fischione, Mikael Skoglund ; PMLR 97:2191-2200

Improved Parallel Algorithms for Density-Based Network Clustering

Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrović ; PMLR 97:2201-2210

Recursive Sketches for Modular Deep Learning

Badih Ghazi, Rina Panigrahy, Joshua Wang ; PMLR 97:2211-2220

An Instability in Variational Inference for Topic Models

Behrooz Ghorbani, Hamid Javadi, Andrea Montanari ; PMLR 97:2221-2231

An Investigation into Neural Net Optimization via Hessian Eigenvalue Density

Behrooz Ghorbani, Shankar Krishnan, Ying Xiao ; PMLR 97:2232-2241

Data Shapley: Equitable Valuation of Data for Machine Learning

Amirata Ghorbani, James Zou ; PMLR 97:2242-2251

Efficient Dictionary Learning with Gradient Descent

Dar Gilboa, Sam Buchanan, John Wright ; PMLR 97:2252-2259

A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes

Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvtiskii ; PMLR 97:2260-2268

Learning to Groove with Inverse Sequence Transformations

Jon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman ; PMLR 97:2269-2279

Adversarial Examples Are a Natural Consequence of Test Error in Noise

Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin Cubuk ; PMLR 97:2280-2289

Discovering Conditionally Salient Features with Statistical Guarantees

Jaime Roquero Gimenez, James Zou ; PMLR 97:2290-2298

Estimating Information Flow in Deep Neural Networks

Ziv Goldfeld, Ewout Van Den Berg, Kristjan Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, Yury Polyanskiy ; PMLR 97:2299-2308

Amortized Monte Carlo Integration

Adam Golinski, Frank Wood, Tom Rainforth ; PMLR 97:2309-2318

Online Algorithms for Rent-Or-Buy with Expert Advice

Sreenivas Gollapudi, Debmalya Panigrahi ; PMLR 97:2319-2327

The information-theoretic value of unlabeled data in semi-supervised learning

Alexander Golovnev, David Pal, Balazs Szorenyi ; PMLR 97:2328-2336

Efficient Training of BERT by Progressively Stacking

Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tieyan Liu ; PMLR 97:2337-2346

Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization

Chengyue Gong, Jian Peng, Qiang Liu ; PMLR 97:2347-2356

Obtaining Fairness using Optimal Transport Theory

Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, Jean-Michel Loubes ; PMLR 97:2357-2365

Combining parametric and nonparametric models for off-policy evaluation

Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez ; PMLR 97:2366-2375

Counterfactual Visual Explanations

Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee ; PMLR 97:2376-2384

Adaptive Sensor Placement for Continuous Spaces

James Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David Leslie, Sattar Vakili, Enrique Munoz De Cote ; PMLR 97:2385-2393

A Statistical Investigation of Long Memory in Language and Music

Alexander Greaves-Tunnell, Zaid Harchaoui ; PMLR 97:2394-2403

Automatic Posterior Transformation for Likelihood-Free Inference

David Greenberg, Marcel Nonnenmacher, Jakob Macke ; PMLR 97:2404-2414

Learning to Optimize Multigrid PDE Solvers

Daniel Greenfeld, Meirav Galun, Ronen Basri, Irad Yavneh, Ron Kimmel ; PMLR 97:2415-2423

Multi-Object Representation Learning with Iterative Variational Inference

Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, Alexander Lerchner ; PMLR 97:2424-2433

Graphite: Iterative Generative Modeling of Graphs

Aditya Grover, Aaron Zweig, Stefano Ermon ; PMLR 97:2434-2444

Fast Algorithm for Generalized Multinomial Models with Ranking Data

Jiaqi Gu, Guosheng Yin ; PMLR 97:2445-2453

Towards a Deep and Unified Understanding of Deep Neural Models in NLP

Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie ; PMLR 97:2454-2463

An Investigation of Model-Free Planning

Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sebastien Racaniere, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap ; PMLR 97:2464-2473

Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops

Limor Gultchin, Genevieve Patterson, Nancy Baym, Nathaniel Swinger, Adam Kalai ; PMLR 97:2474-2483

Simple Black-box Adversarial Attacks

Chuan Guo, Jacob Gardner, Yurong You, Andrew Gordon Wilson, Kilian Weinberger ; PMLR 97:2484-2493

Exploring interpretable LSTM neural networks over multi-variable data

Tian Guo, Tao Lin, Nino Antulov-Fantulin ; PMLR 97:2494-2504

Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs

Lingbing Guo, Zequn Sun, Wei Hu ; PMLR 97:2505-2514

Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications

Albert Gural, Boris Murmann ; PMLR 97:2515-2524

IMEXnet A Forward Stable Deep Neural Network

Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto ; PMLR 97:2525-2534

On The Power of Curriculum Learning in Training Deep Networks

Guy Hacohen, Daphna Weinshall ; PMLR 97:2535-2544

Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization

Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck Cadambe ; PMLR 97:2545-2554

Learning Latent Dynamics for Planning from Pixels

Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson ; PMLR 97:2555-2565

Neural Separation of Observed and Unobserved Distributions

Tavi Halperin, Ariel Ephrat, Yedid Hoshen ; PMLR 97:2566-2575

Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI

Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, Tong Zhang ; PMLR 97:2576-2585

Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning

Seungyul Han, Youngchul Sung ; PMLR 97:2586-2595

Complexity of Linear Regions in Deep Networks

Boris Hanin, David Rolnick ; PMLR 97:2596-2604

Importance Sampling Policy Evaluation with an Estimated Behavior Policy

Josiah Hanna, Scott Niekum, Peter Stone ; PMLR 97:2605-2613

Doubly-Competitive Distribution Estimation

Yi Hao, Alon Orlitsky ; PMLR 97:2614-2623

Random Shuffling Beats SGD after Finite Epochs

Jeff Haochen, Suvrit Sra ; PMLR 97:2624-2633

Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications

Chris Harshaw, Moran Feldman, Justin Ward, Amin Karbasi ; PMLR 97:2634-2643

Per-Decision Option Discounting

Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowe, Doina Precup ; PMLR 97:2644-2652

Submodular Observation Selection and Information Gathering for Quadratic Models

Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo, Ufuk Topcu ; PMLR 97:2653-2662

Understanding and Controlling Memory in Recurrent Neural Networks

Doron Haviv, Alexander Rivkind, Omri Barak ; PMLR 97:2663-2671

On the Impact of the Activation function on Deep Neural Networks Training

Soufiane Hayou, Arnaud Doucet, Judith Rousseau ; PMLR 97:2672-2680

Provably Efficient Maximum Entropy Exploration

Elad Hazan, Sham Kakade, Karan Singh, Abby Van Soest ; PMLR 97:2681-2691

On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning

Hoda Heidari, Vedant Nanda, Krishna Gummadi ; PMLR 97:2692-2701

Graph Resistance and Learning from Pairwise Comparisons

Julien Hendrickx, Alexander Olshevsky, Venkatesh Saligrama ; PMLR 97:2702-2711

Using Pre-Training Can Improve Model Robustness and Uncertainty

Dan Hendrycks, Kimin Lee, Mantas Mazeika ; PMLR 97:2712-2721

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel ; PMLR 97:2722-2730

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, Pieter Abbeel ; PMLR 97:2731-2741

Collective Model Fusion for Multiple Black-Box Experts

Minh Hoang, Nghia Hoang, Bryan Kian Hsiang Low, Carleton Kingsford ; PMLR 97:2742-2750

Connectivity-Optimized Representation Learning via Persistent Homology

Christoph Hofer, Roland Kwitt, Marc Niethammer, Mandar Dixit ; PMLR 97:2751-2760

Better generalization with less data using robust gradient descent

Matthew Holland, Kazushi Ikeda ; PMLR 97:2761-2770

Emerging Convolutions for Generative Normalizing Flows

Emiel Hoogeboom, Rianne Van Den Berg, Max Welling ; PMLR 97:2771-2780

Nonconvex Variance Reduced Optimization with Arbitrary Sampling

Samuel Horváth, Peter Richtarik ; PMLR 97:2781-2789

Parameter-Efficient Transfer Learning for NLP

Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly ; PMLR 97:2790-2799

Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging

Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P R Kumar ; PMLR 97:2800-2809

Finding Mixed Nash Equilibria of Generative Adversarial Networks

Ya-Ping Hsieh, Chen Liu, Volkan Cevher ; PMLR 97:2810-2819

Classification from Positive, Unlabeled and Biased Negative Data

Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama ; PMLR 97:2820-2829

Bayesian Deconditional Kernel Mean Embeddings

Kelvin Hsu, Fabio Ramos ; PMLR 97:2830-2838

Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization

Feihu Huang, Songcan Chen, Heng Huang ; PMLR 97:2839-2848

Unsupervised Deep Learning by Neighbourhood Discovery

Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu ; PMLR 97:2849-2858

Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm

Kejun Huang, Xiao Fu ; PMLR 97:2859-2868

Hierarchical Importance Weighted Autoencoders

Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville ; PMLR 97:2869-2878

Stable and Fair Classification

Lingxiao Huang, Nisheeth Vishnoi ; PMLR 97:2879-2890

Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment

Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Bautista Martin, Shih-Yu Sun, Carlos Guestrin, Josh Susskind ; PMLR 97:2891-2900

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models

Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour ; PMLR 97:2901-2910

Composing Entropic Policies using Divergence Correction

Jonathan Hunt, Andre Barreto, Timothy Lillicrap, Nicolas Heess ; PMLR 97:2911-2920

HexaGAN: Generative Adversarial Nets for Real World Classification

Uiwon Hwang, Dahuin Jung, Sungroh Yoon ; PMLR 97:2921-2930

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

Alessandro Davide Ialongo, Mark Van Der Wilk, James Hensman, Carl Edward Rasmussen ; PMLR 97:2931-2940

Learning Structured Decision Problems with Unawareness

Craig Innes, Alex Lascarides ; PMLR 97:2941-2950

Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!

Niels Ipsen, Lars Kai Hansen ; PMLR 97:2951-2960

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

Shariq Iqbal, Fei Sha ; PMLR 97:2961-2970

Complementary-Label Learning for Arbitrary Losses and Models

Takashi Ishida, Gang Niu, Aditya Menon, Masashi Sugiyama ; PMLR 97:2971-2980

Causal Identification under Markov Equivalence: Completeness Results

Amin Jaber, Jiji Zhang, Elias Bareinboim ; PMLR 97:2981-2989

Learning from a Learner

Alexis Jacq, Matthieu Geist, Ana Paiva, Olivier Pietquin ; PMLR 97:2990-2999

Differentially Private Fair Learning

Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi -Malvajerdi, Jonathan Ullman ; PMLR 97:3000-3008

Sum-of-Squares Polynomial Flow

Priyank Jaini, Kira A. Selby, Yaoliang Yu ; PMLR 97:3009-3018

DBSCAN++: Towards fast and scalable density clustering

Jennifer Jang, Heinrich Jiang ; PMLR 97:3019-3029

Learning What and Where to Transfer

Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin ; PMLR 97:3030-3039

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro Ortega, Dj Strouse, Joel Z. Leibo, Nando De Freitas ; PMLR 97:3040-3049

A Deep Reinforcement Learning Perspective on Internet Congestion Control

Nathan Jay, Noga Rotman, Brighten Godfrey, Michael Schapira, Aviv Tamar ; PMLR 97:3050-3059

Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance

Dasaem Jeong, Taegyun Kwon, Yoojin Kim, Juhan Nam ; PMLR 97:3060-3070

Ladder Capsule Network

Taewon Jeong, Youngmin Lee, Heeyoung Kim ; PMLR 97:3071-3079

Training CNNs with Selective Allocation of Channels

Jongheon Jeong, Jinwoo Shin ; PMLR 97:3080-3090

Learning Discrete and Continuous Factors of Data via Alternating Disentanglement

Yeonwoo Jeong, Hyun Oh Song ; PMLR 97:3091-3099

Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization

Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang ; PMLR 97:3100-3109

Neural Logic Reinforcement Learning

Zhengyao Jiang, Shan Luo ; PMLR 97:3110-3119

Finding Options that Minimize Planning Time

Yuu Jinnai, David Abel, David Hershkowitz, Michael Littman, George Konidaris ; PMLR 97:3120-3129

Discovering Options for Exploration by Minimizing Cover Time

Yuu Jinnai, Jee Won Park, David Abel, George Konidaris ; PMLR 97:3130-3139

Kernel Mean Matching for Content Addressability of GANs

Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf ; PMLR 97:3140-3151

GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver

David John, Vincent Heuveline, Michael Schober ; PMLR 97:3152-3162

Bilinear Bandits with Low-rank Structure

Kwang-Sung Jun, Rebecca Willett, Stephen Wright, Robert Nowak ; PMLR 97:3163-3172

Statistical Foundations of Virtual Democracy

Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel Procaccia, Christos-Alexandros Psomas ; PMLR 97:3173-3182

Molecular Hypergraph Grammar with Its Application to Molecular Optimization

Hiroshi Kajino ; PMLR 97:3183-3191

Robust Influence Maximization for Hyperparametric Models

Dimitris Kalimeris, Gal Kaplun, Yaron Singer ; PMLR 97:3192-3200

Classifying Treatment Responders Under Causal Effect Monotonicity

Nathan Kallus ; PMLR 97:3201-3210

Trainable Decoding of Sets of Sequences for Neural Sequence Models

Ashwin Kalyan, Peter Anderson, Stefan Lee, Dhruv Batra ; PMLR 97:3211-3221

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments

Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos ; PMLR 97:3222-3232

Differentially Private Learning of Geometric Concepts

Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer ; PMLR 97:3233-3241

Policy Consolidation for Continual Reinforcement Learning

Christos Kaplanis, Murray Shanahan, Claudia Clopath ; PMLR 97:3242-3251

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian Stich, Martin Jaggi ; PMLR 97:3252-3261

Riemannian adaptive stochastic gradient algorithms on matrix manifolds

Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra ; PMLR 97:3262-3271

Neural Inverse Knitting: From Images to Manufacturing Instructions

Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Wojciech Matusik ; PMLR 97:3272-3281

Processing Megapixel Images with Deep Attention-Sampling Models

Angelos Katharopoulos, Francois Fleuret ; PMLR 97:3282-3291

Robust Estimation of Tree Structured Gaussian Graphical Models

Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis ; PMLR 97:3292-3300

Shallow-Deep Networks: Understanding and Mitigating Network Overthinking

Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras ; PMLR 97:3301-3310

Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity

Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam, Silvio Lattanzi, Amin Karbasi ; PMLR 97:3311-3320

Adaptive Scale-Invariant Online Algorithms for Learning Linear Models

Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth ; PMLR 97:3321-3330

CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network

Tom Kenter, Vincent Wan, Chun-An Chan, Rob Clark, Jakub Vit ; PMLR 97:3331-3340

Collaborative Evolutionary Reinforcement Learning

Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer ; PMLR 97:3341-3350

Geometry Aware Convolutional Filters for Omnidirectional Images Representation

Renata Khasanova, Pascal Frossard ; PMLR 97:3351-3359

EMI: Exploration with Mutual Information

Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song ; PMLR 97:3360-3369

FloWaveNet : A Generative Flow for Raw Audio

Sungwon Kim, Sang-Gil Lee, Jongyoon Song, Jaehyeon Kim, Sungroh Yoon ; PMLR 97:3370-3378

Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty

Youngjin Kim, Wontae Nam, Hyunwoo Kim, Ji-Hoon Kim, Gunhee Kim ; PMLR 97:3379-3388

Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model

Gi-Soo Kim, Myunghee Cho Paik ; PMLR 97:3389-3397

Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension

Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo, Larry Wasserman ; PMLR 97:3398-3407

Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables

Friso Kingma, Pieter Abbeel, Jonathan Ho ; PMLR 97:3408-3417

CompILE: Compositional Imitation Learning and Execution

Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter Battaglia ; PMLR 97:3418-3428

Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces

Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, Andreas Krause ; PMLR 97:3429-3438

AUCμ: A Performance Metric for Multi-Class Machine Learning Models

Ross Kleiman, David Page ; PMLR 97:3439-3447

Fair k-Center Clustering for Data Summarization

Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern ; PMLR 97:3448-3457

Guarantees for Spectral Clustering with Fairness Constraints

Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern ; PMLR 97:3458-3467

POPQORN: Quantifying Robustness of Recurrent Neural Networks

Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, Dahua Lin ; PMLR 97:3468-3477

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication

Anastasia Koloskova, Sebastian Stich, Martin Jaggi ; PMLR 97:3478-3487

Robust Learning from Untrusted Sources

Nikola Konstantinov, Christoph Lampert ; PMLR 97:3488-3498

Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

Wouter Kool, Herke Van Hoof, Max Welling ; PMLR 97:3499-3508

LIT: Learned Intermediate Representation Training for Model Compression

Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia ; PMLR 97:3509-3518

Similarity of Neural Network Representations Revisited

Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey Hinton ; PMLR 97:3519-3529

On the Complexity of Approximating Wasserstein Barycenters

Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel Dvurechensky, Alexander Gasnikov, Cesar Uribe ; PMLR 97:3530-3540

Estimate Sequences for Variance-Reduced Stochastic Composite Optimization

Andrei Kulunchakov, Julien Mairal ; PMLR 97:3541-3550

Faster Algorithms for Binary Matrix Factorization

Ravi Kumar, Rina Panigrahy, Ali Rahimi, David Woodruff ; PMLR 97:3551-3559

Loss Landscapes of Regularized Linear Autoencoders

Daniel Kunin, Jonathan Bloom, Aleksandrina Goeva, Cotton Seed ; PMLR 97:3560-3569

Geometry and Symmetry in Short-and-Sparse Deconvolution

Han-Wen Kuo, Yenson Lau, Yuqian Zhang, John Wright ; PMLR 97:3570-3580

A Large-Scale Study on Regularization and Normalization in GANs

Karol Kurach, Mario Lučić, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly ; PMLR 97:3581-3590

Making Decisions that Reduce Discriminatory Impacts

Matt Kusner, Chris Russell, Joshua Loftus, Ricardo Silva ; PMLR 97:3591-3600

Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits

Branislav Kveton, Csaba Szepesvari, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh ; PMLR 97:3601-3610

Characterizing Well-Behaved vs. Pathological Deep Neural Networks

Antoine Labatie ; PMLR 97:3611-3621

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations

Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer ; PMLR 97:3622-3631

A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion

Sylvain Lamprier ; PMLR 97:3632-3641

Projection onto Minkowski Sums with Application to Constrained Learning

Joong-Ho Won, Jason Xu, Kenneth Lange ; PMLR 97:3642-3651

Safe Policy Improvement with Baseline Bootstrapping

Romain Laroche, Paul Trichelair, Remi Tachet Des Combes ; PMLR 97:3652-3661

A Better k-means++ Algorithm via Local Search

Silvio Lattanzi, Christian Sohler ; PMLR 97:3662-3671

Lorentzian Distance Learning for Hyperbolic Representations

Marc Law, Renjie Liao, Jake Snell, Richard Zemel ; PMLR 97:3672-3681

DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

Andrew Lawrence, Carl Henrik Ek, Neill Campbell ; PMLR 97:3682-3691

POLITEX: Regret Bounds for Policy Iteration using Expert Prediction

Yasin Abbasi-Yadkori, Peter Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvari, Gellert Weisz ; PMLR 97:3692-3702

Batch Policy Learning under Constraints

Hoang Le, Cameron Voloshin, Yisong Yue ; PMLR 97:3703-3712

Target-Based Temporal-Difference Learning

Donghwan Lee, Niao He ; PMLR 97:3713-3722

Functional Transparency for Structured Data: a Game-Theoretic Approach

Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi Jaakkola ; PMLR 97:3723-3733

Self-Attention Graph Pooling

Junhyun Lee, Inyeop Lee, Jaewoo Kang ; PMLR 97:3734-3743

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, Yee Whye Teh ; PMLR 97:3744-3753

First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems

Ching-Pei Lee, Stephen Wright ; PMLR 97:3754-3762

Robust Inference via Generative Classifiers for Handling Noisy Labels

Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin ; PMLR 97:3763-3772

Sublinear Time Nearest Neighbor Search over Generalized Weighted Space

Yifan Lei, Qiang Huang, Mohan Kankanhalli, Anthony Tung ; PMLR 97:3773-3781

MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means

Matthieu Lerasle, Zoltan Szabo, Timothée Mathieu, Guillaume Lecue ; PMLR 97:3782-3793

Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group

Mario Lezcano-Casado, David Martı́nez-Rubio ; PMLR 97:3794-3803

Are Generative Classifiers More Robust to Adversarial Attacks?

Yingzhen Li, John Bradshaw, Yash Sharma ; PMLR 97:3804-3814

Sublinear quantum algorithms for training linear and kernel-based classifiers

Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu ; PMLR 97:3815-3824

LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning

Huaiyu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu ; PMLR 97:3825-3834

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli ; PMLR 97:3835-3845

Area Attention

Yang Li, Lukasz Kaiser, Samy Bengio, Si Si ; PMLR 97:3846-3855

Online Learning to Rank with Features

Shuai Li, Tor Lattimore, Csaba Szepesvari ; PMLR 97:3856-3865

NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks

Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong ; PMLR 97:3866-3876

Bayesian Joint Spike-and-Slab Graphical Lasso

Zehang Li, Tyler Mccormick, Samuel Clark ; PMLR 97:3877-3885

Exploiting Worker Correlation for Label Aggregation in Crowdsourcing

Yuan Li, Benjamin Rubinstein, Trevor Cohn ; PMLR 97:3886-3895

Adversarial camera stickers: A physical camera-based attack on deep learning systems

Juncheng Li, Frank Schmidt, Zico Kolter ; PMLR 97:3896-3904

Towards a Unified Analysis of Random Fourier Features

Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic ; PMLR 97:3905-3914

Feature-Critic Networks for Heterogeneous Domain Generalization

Yiying Li, Yongxin Yang, Wei Zhou, Timothy Hospedales ; PMLR 97:3915-3924

Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting

Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, Caiming Xiong ; PMLR 97:3925-3934

Alternating Minimizations Converge to Second-Order Optimal Solutions

Qiuwei Li, Zhihui Zhu, Gongguo Tang ; PMLR 97:3935-3943

Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints

Nikolaos Liakopoulos, Apostolos Destounis, Georgios Paschos, Thrasyvoulos Spyropoulos, Panayotis Mertikopoulos ; PMLR 97:3944-3952

Regularization in directable environments with application to Tetris

Jan Malte Lichtenberg, Özgür Şimşek ; PMLR 97:3953-3962

Inference and Sampling of $K_33$-free Ising Models

Valerii Likhosherstov, Yury Maximov, Misha Chertkov ; PMLR 97:3963-3972

Kernel-Based Reinforcement Learning in Robust Markov Decision Processes

Shiau Hong Lim, Arnaud Autef ; PMLR 97:3973-3981

On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms

Tianyi Lin, Nhat Ho, Michael Jordan ; PMLR 97:3982-3991

Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations

Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt ; PMLR 97:3992-4002

Acceleration of SVRG and Katyusha X by Inexact Preconditioning

Yanli Liu, Fei Feng, Wotao Yin ; PMLR 97:4003-4012

Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers

Hong Liu, Mingsheng Long, Jianmin Wang, Michael Jordan ; PMLR 97:4013-4022

Rao-Blackwellized Stochastic Gradients for Discrete Distributions

Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael Jordan, Jon Mcauliffe ; PMLR 97:4023-4031

Sparse Extreme Multi-label Learning with Oracle Property

Weiwei Liu, Xiaobo Shen ; PMLR 97:4032-4041

Data Poisoning Attacks on Stochastic Bandits

Fang Liu, Ness Shroff ; PMLR 97:4042-4050

The Implicit Fairness Criterion of Unconstrained Learning

Lydia T. Liu, Max Simchowitz, Moritz Hardt ; PMLR 97:4051-4060

Taming MAML: Efficient unbiased meta-reinforcement learning

Hao Liu, Richard Socher, Caiming Xiong ; PMLR 97:4061-4071

On Certifying Non-Uniform Bounds against Adversarial Attacks

Chen Liu, Ryota Tomioka, Volkan Cevher ; PMLR 97:4072-4081

Understanding and Accelerating Particle-Based Variational Inference

Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu ; PMLR 97:4082-4092

Understanding MCMC Dynamics as Flows on the Wasserstein Space

Chang Liu, Jingwei Zhuo, Jun Zhu ; PMLR 97:4093-4103

Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions

Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter ; PMLR 97:4104-4113

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem ; PMLR 97:4114-4124

Bayesian Counterfactual Risk Minimization

Ben London, Ted Sandler ; PMLR 97:4125-4133

PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization

Songtao Lu, Mingyi Hong, Zhengdao Wang ; PMLR 97:4134-4143

Neurally-Guided Structure Inference

Sidi Lu, Jiayuan Mao, Joshua Tenenbaum, Jiajun Wu ; PMLR 97:4144-4153

Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards

Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang ; PMLR 97:4154-4163

CoT: Cooperative Training for Generative Modeling of Discrete Data

Sidi Lu, Lantao Yu, Siyuan Feng, Yaoming Zhu, Weinan Zhang ; PMLR 97:4164-4172

Generalized Approximate Survey Propagation for High-Dimensional Estimation

Carlo Lucibello, Luca Saglietti, Yue Lu ; PMLR 97:4173-4182

High-Fidelity Image Generation With Fewer Labels

Mario Lučić, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly ; PMLR 97:4183-4192

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction

Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto ; PMLR 97:4193-4202

Differentiable Dynamic Normalization for Learning Deep Representation

Ping Luo, Peng Zhanglin, Shao Wenqi, Zhang Ruimao, Ren Jiamin, Wu Lingyun ; PMLR 97:4203-4211

Disentangled Graph Convolutional Networks

Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu ; PMLR 97:4212-4221

Variational Implicit Processes

Chao Ma, Yingzhen Li, Jose Miguel Hernandez-Lobato ; PMLR 97:4222-4233

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez-Lobato, Sebastian Nowozin, Cheng Zhang ; PMLR 97:4234-4243

Bayesian leave-one-out cross-validation for large data

Måns Magnusson, Michael Andersen, Johan Jonasson, Aki Vehtari ; PMLR 97:4244-4253

Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm

Sepideh Mahabadi, Piotr Indyk, Shayan Oveis Gharan, Alireza Rezaei ; PMLR 97:4254-4263

Guided evolutionary strategies: augmenting random search with surrogate gradients

Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein ; PMLR 97:4264-4273

Data Poisoning Attacks in Multi-Party Learning

Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed ; PMLR 97:4274-4283

Traditional and Heavy Tailed Self Regularization in Neural Network Models

Michael Mahoney, Charles Martin ; PMLR 97:4284-4293

Curvature-Exploiting Acceleration of Elastic Net Computations

Vien Mai, Mikael Johansson ; PMLR 97:4294-4303

Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms

Ashok Makkuva, Pramod Viswanath, Sreeram Kannan, Sewoong Oh ; PMLR 97:4304-4313

Calibrated Model-Based Deep Reinforcement Learning

Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon ; PMLR 97:4314-4323

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems

Timothy Arthur Mann, Sven Gowal, Andras Gyorgy, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan ; PMLR 97:4324-4332

Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models

Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborova ; PMLR 97:4333-4342

A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs

Jingkai Mao, Jakob Foerster, Tim Rocktäschel, Maruan Al-Shedivat, Gregory Farquhar, Shimon Whiteson ; PMLR 97:4343-4351

Adversarial Generation of Time-Frequency Features with application in audio synthesis

Andrés Marafioti, Nathanaël Perraudin, Nicki Holighaus, Piotr Majdak ; PMLR 97:4352-4362

On the Universality of Invariant Networks

Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman ; PMLR 97:4363-4371

Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models

Kaspar Märtens, Kieran Campbell, Christopher Yau ; PMLR 97:4372-4381

Fairness-Aware Learning for Continuous Attributes and Treatments

Jeremie Mary, Clément Calauzènes, Noureddine El Karoui ; PMLR 97:4382-4391

Optimal Minimal Margin Maximization with Boosting

Alexander Mathiasen, Kasper Green Larsen, Allan Grønlund ; PMLR 97:4392-4401

Disentangling Disentanglement in Variational Autoencoders

Emile Mathieu, Tom Rainforth, N Siddharth, Yee Whye Teh ; PMLR 97:4402-4412

MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets

Pierre-Alexandre Mattei, Jes Frellsen ; PMLR 97:4413-4423

Distributional Reinforcement Learning for Efficient Exploration

Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu ; PMLR 97:4424-4434

Graphical-model based estimation and inference for differential privacy

Ryan Mckenna, Daniel Sheldon, Gerome Miklau ; PMLR 97:4435-4444

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

Ted Meeds, Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau ; PMLR 97:4445-4455

Toward Controlling Discrimination in Online Ad Auctions

Elisa Celis, Anay Mehrotra, Nisheeth Vishnoi ; PMLR 97:4456-4465

Stochastic Blockmodels meet Graph Neural Networks

Nikhil Mehta, Lawrence Carin Duke, Piyush Rai ; PMLR 97:4466-4474

Imputing Missing Events in Continuous-Time Event Streams

Hongyuan Mei, Guanghui Qin, Jason Eisner ; PMLR 97:4475-4485

Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization

Eldad Meller, Alexander Finkelstein, Uri Almog, Mark Grobman ; PMLR 97:4486-4495

The Wasserstein Transform

Facundo Memoli, Zane Smith, Zhengchao Wan ; PMLR 97:4496-4504

Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks

Charith Mendis, Alex Renda, Dr.Saman Amarasinghe, Michael Carbin ; PMLR 97:4505-4515

Geometric Losses for Distributional Learning

Arthur Mensch, Mathieu Blondel, Gabriel Peyré ; PMLR 97:4516-4525

Spectral Clustering of Signed Graphs via Matrix Power Means

Pedro Mercado, Francesco Tudisco, Matthias Hein ; PMLR 97:4526-4536

Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization

Michael Metel, Akiko Takeda ; PMLR 97:4537-4545

Reinforcement Learning in Configurable Continuous Environments

Alberto Maria Metelli, Emanuele Ghelfi, Marcello Restelli ; PMLR 97:4546-4555

Understanding and correcting pathologies in the training of learned optimizers

Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, Jascha Sohl-Dickstein ; PMLR 97:4556-4565

Optimality Implies Kernel Sum Classifiers are Statistically Efficient

Raphael Meyer, Jean Honorio ; PMLR 97:4566-4574

On Dropout and Nuclear Norm Regularization

Poorya Mianjy, Raman Arora ; PMLR 97:4575-4584

Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography

Andrew Miller, Ziad Obermeyer, John Cunningham, Sendhil Mullainathan ; PMLR 97:4585-4594

Formal Privacy for Functional Data with Gaussian Perturbations

Ardalan Mirshani, Matthew Reimherr, Aleksandra Slavković ; PMLR 97:4595-4604

Co-manifold learning with missing data

Gal Mishne, Eric Chi, Ronald Coifman ; PMLR 97:4605-4614

Agnostic Federated Learning

Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh ; PMLR 97:4615-4625

Flat Metric Minimization with Applications in Generative Modeling

Thomas Möllenhoff, Daniel Cremers ; PMLR 97:4626-4635

Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization

Seungyong Moon, Gaon An, Hyun Oh Song ; PMLR 97:4636-4645

Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization

Hesham Mostafa, Xin Wang ; PMLR 97:4646-4655

A Dynamical Systems Perspective on Nesterov Acceleration

Michael Muehlebach, Michael Jordan ; PMLR 97:4656-4662

Relational Pooling for Graph Representations

Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro ; PMLR 97:4663-4673

Learning Optimal Fair Policies

Razieh Nabi, Daniel Malinsky, Ilya Shpitser ; PMLR 97:4674-4682

Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models

Mor Shpigel Nacson, Suriya Gunasekar, Jason Lee, Nathan Srebro, Daniel Soudry ; PMLR 97:4683-4692

A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning

Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama ; PMLR 97:4693-4702

SGD without Replacement: Sharper Rates for General Smooth Convex Functions

Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli ; PMLR 97:4703-4711

Dropout as a Structured Shrinkage Prior

Eric Nalisnick, Jose Miguel Hernandez-Lobato, Padhraic Smyth ; PMLR 97:4712-4722

Hybrid Models with Deep and Invertible Features

Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan ; PMLR 97:4723-4732

Learning Context-dependent Label Permutations for Multi-label Classification

Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencia, Sunghyun Park, Ruhi Sarikaya, Johannes Fürnkranz ; PMLR 97:4733-4742

Zero-Shot Knowledge Distillation in Deep Networks

Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, Anirban Chakraborty ; PMLR 97:4743-4751

A Framework for Bayesian Optimization in Embedded Subspaces

Amin Nayebi, Alexander Munteanu, Matthias Poloczek ; PMLR 97:4752-4761

Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements

Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani ; PMLR 97:4762-4770

Safe Grid Search with Optimal Complexity

Eugene Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi ; PMLR 97:4771-4780

Learning to bid in revenue-maximizing auctions

Thomas Nedelec, Noureddine El Karoui, Vianney Perchet ; PMLR 97:4781-4789

On Connected Sublevel Sets in Deep Learning

Quynh Nguyen ; PMLR 97:4790-4799

Anomaly Detection With Multiple-Hypotheses Predictions

Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox ; PMLR 97:4800-4809

Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization

Than Huy Nguyen, Umut Simsekli, Gael Richard ; PMLR 97:4810-4819

Rotation Invariant Householder Parameterization for Bayesian PCA

Rajbir Nirwan, Nils Bertschinger ; PMLR 97:4820-4828

Lossless or Quantized Boosting with Integer Arithmetic

Richard Nock, Robert Williamson ; PMLR 97:4829-4838

Training Neural Networks with Local Error Signals

Arild Nøkland, Lars Hiller Eidnes ; PMLR 97:4839-4850

Remember and Forget for Experience Replay

Guido Novati, Petros Koumoutsakos ; PMLR 97:4851-4860

Learning to Infer Program Sketches

Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, Armando Solar-Lezama ; PMLR 97:4861-4870

Tensor Variable Elimination for Plated Factor Graphs

Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin Chiu, Alexander Rush, Noah Goodman ; PMLR 97:4871-4880

Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models

Michael Oberst, David Sontag ; PMLR 97:4881-4890

Model Function Based Conditional Gradient Method with Armijo-like Line Search

Peter Ochs, Yura Malitsky ; PMLR 97:4891-4900

TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing

Augustus Odena, Catherine Olsson, David Andersen, Ian Goodfellow ; PMLR 97:4901-4911

Scalable Learning in Reproducing Kernel Krein Spaces

Dino Oglic, Thomas Gärtner ; PMLR 97:4912-4921

Approximation and non-parametric estimation of ResNet-type convolutional neural networks

Kenta Oono, Taiji Suzuki ; PMLR 97:4922-4931

Orthogonal Random Forest for Causal Inference

Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu ; PMLR 97:4932-4941

Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding

Muhammad Osama, Dave Zachariah, Thomas B. Schön ; PMLR 97:4942-4950

Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?

Samet Oymak, Mahdi Soltanolkotabi ; PMLR 97:4951-4960

Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always

Ioannis Panageas, Georgios Piliouras, Xiao Wang ; PMLR 97:4961-4969

Improving Adversarial Robustness via Promoting Ensemble Diversity

Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu ; PMLR 97:4970-4979

Nonparametric Bayesian Deep Networks with Local Competition

Konstantinos Panousis, Sotirios Chatzis, Sergios Theodoridis ; PMLR 97:4980-4988

Optimistic Policy Optimization via Multiple Importance Sampling

Matteo Papini, Alberto Maria Metelli, Lorenzo Lupo, Marcello Restelli ; PMLR 97:4989-4999

Deep Residual Output Layers for Neural Language Generation

Nikolaos Pappas, James Henderson ; PMLR 97:5000-5011

Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians

Vardan Papyan ; PMLR 97:5012-5021

Generalized Majorization-Minimization

Sobhan Naderi Parizi, Kun He, Reza Aghajani, Stan Sclaroff, Pedro Felzenszwalb ; PMLR 97:5022-5031

Variational Laplace Autoencoders

Yookoon Park, Chris Kim, Gunhee Kim ; PMLR 97:5032-5041

The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study

Daniel Park, Jascha Sohl-Dickstein, Quoc Le, Samuel Smith ; PMLR 97:5042-5051

Spectral Approximate Inference

Sejun Park, Eunho Yang, Se-Young Yun, Jinwoo Shin ; PMLR 97:5052-5061

Self-Supervised Exploration via Disagreement

Deepak Pathak, Dhiraj Gandhi, Abhinav Gupta ; PMLR 97:5062-5071

Subspace Robust Wasserstein Distances

François-Pierre Paty, Marco Cuturi ; PMLR 97:5072-5081

Fingerprint Policy Optimisation for Robust Reinforcement Learning

Supratik Paul, Michael A. Osborne, Shimon Whiteson ; PMLR 97:5082-5091

COMIC: Multi-view Clustering Without Parameter Selection

Xi Peng, Zhenyu Huang, Jiancheng Lv, Hongyuan Zhu, Joey Tianyi Zhou ; PMLR 97:5092-5101

Domain Agnostic Learning with Disentangled Representations

Xingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenko ; PMLR 97:5102-5112

Collaborative Channel Pruning for Deep Networks

Hanyu Peng, Jiaxiang Wu, Shifeng Chen, Junzhou Huang ; PMLR 97:5113-5122

Exploiting structure of uncertainty for efficient matroid semi-bandits

Pierre Perrault, Vianney Perchet, Michal Valko ; PMLR 97:5123-5132

Cognitive model priors for predicting human decisions

David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths ; PMLR 97:5133-5141

Towards Understanding Knowledge Distillation

Mary Phuong, Christoph Lampert ; PMLR 97:5142-5151

Temporal Gaussian Mixture Layer for Videos

Aj Piergiovanni, Michael Ryoo ; PMLR 97:5152-5161

Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration

Vladislav Polianskii, Florian T. Pokorny ; PMLR 97:5162-5170

On Variational Bounds of Mutual Information

Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, George Tucker ; PMLR 97:5171-5180

Hiring Under Uncertainty

Manish Purohit, Sreenivas Gollapudi, Manish Raghavan ; PMLR 97:5181-5189

SAGA with Arbitrary Sampling

Xun Qian, Zheng Qu, Peter Richtárik ; PMLR 97:5190-5199

SGD: General Analysis and Improved Rates

Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik ; PMLR 97:5200-5209

AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Mark Hasegawa-Johnson ; PMLR 97:5210-5219

Fault Tolerance in Iterative-Convergent Machine Learning

Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric Xing ; PMLR 97:5220-5230

Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition

Yao Qin, Nicholas Carlini, Garrison Cottrell, Ian Goodfellow, Colin Raffel ; PMLR 97:5231-5240

GMNN: Graph Markov Neural Networks

Meng Qu, Yoshua Bengio, Jian Tang ; PMLR 97:5241-5250

Nonlinear Distributional Gradient Temporal-Difference Learning

Chao Qu, Shie Mannor, Huan Xu ; PMLR 97:5251-5260

Learning to Collaborate in Markov Decision Processes

Goran Radanovic, Rati Devidze, David Parkes, Adish Singla ; PMLR 97:5261-5270

Meta-Learning Neural Bloom Filters

Jack Rae, Sergey Bartunov, Timothy Lillicrap ; PMLR 97:5271-5280

Direct Uncertainty Prediction for Medical Second Opinions

Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Bobby Kleinberg, Sendhil Mullainathan, Jon Kleinberg ; PMLR 97:5281-5290

Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function

Arvind Raghunathan, Anoop Cherian, Devesh Jha ; PMLR 97:5291-5300

On the Spectral Bias of Neural Networks

Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, Aaron Courville ; PMLR 97:5301-5310

Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation

Tahrima Rahman, Shasha Jin, Vibhav Gogate ; PMLR 97:5311-5320

Does Data Augmentation Lead to Positive Margin?

Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris Papailiopoulos ; PMLR 97:5321-5330

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen ; PMLR 97:5331-5340

Screening rules for Lasso with non-convex Sparse Regularizers

Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon ; PMLR 97:5341-5350

Topological Data Analysis of Decision Boundaries with Application to Model Selection

Karthikeyan Natesan Ramamurthy, Kush Varshney, Krishnan Mody ; PMLR 97:5351-5360

HyperGAN: A Generative Model for Diverse, Performant Neural Networks

Neale Ratzlaff, Li Fuxin ; PMLR 97:5361-5369

Efficient On-Device Models using Neural Projections

Sujith Ravi ; PMLR 97:5370-5379

A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation

Ramin Raziperchikolaei, Harish Bhat ; PMLR 97:5380-5388

Do ImageNet Classifiers Generalize to ImageNet?

Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar ; PMLR 97:5389-5400

Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise

Henry Reeve, Ata Kaban ; PMLR 97:5401-5409

Almost Unsupervised Text to Speech and Automatic Speech Recognition

Yi Ren, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu ; PMLR 97:5410-5419

Adaptive Antithetic Sampling for Variance Reduction

Hongyu Ren, Shengjia Zhao, Stefano Ermon ; PMLR 97:5420-5428

Adversarial Online Learning with noise

Alon Resler, Yishay Mansour ; PMLR 97:5429-5437

A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes

Alireza Rezaei, Shayan Oveis Gharan ; PMLR 97:5438-5447

A Persistent Weisfeiler-Lehman Procedure for Graph Classification

Bastian Rieck, Christian Bock, Karsten Borgwardt ; PMLR 97:5448-5458

Efficient learning of smooth probability functions from Bernoulli tests with guarantees

Paul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher ; PMLR 97:5459-5467

Separating value functions across time-scales

Joshua Romoff, Peter Henderson, Ahmed Touati, Emma Brunskill, Joelle Pineau, Yann Ollivier ; PMLR 97:5468-5477

Online Convex Optimization in Adversarial Markov Decision Processes

Aviv Rosenberg, Yishay Mansour ; PMLR 97:5478-5486

Good Initializations of Variational Bayes for Deep Models

Simone Rossi, Pietro Michiardi, Maurizio Filippone ; PMLR 97:5487-5497

The Odds are Odd: A Statistical Test for Detecting Adversarial Examples

Kevin Roth, Yannic Kilcher, Thomas Hofmann ; PMLR 97:5498-5507

Neuron birth-death dynamics accelerates gradient descent and converges asymptotically

Grant Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden ; PMLR 97:5508-5517

Iterative Linearized Control: Stable Algorithms and Complexity Guarantees

Vincent Roulet, Dmitriy Drusvyatskiy, Siddhartha Srinivasa, Zaid Harchaoui ; PMLR 97:5518-5527

Statistics and Samples in Distributional Reinforcement Learning

Mark Rowland, Robert Dadashi, Saurabh Kumar, Remi Munos, Marc G. Bellemare, Will Dabney ; PMLR 97:5528-5536

A Contrastive Divergence for Combining Variational Inference and MCMC

Francisco Ruiz, Michalis Titsias ; PMLR 97:5537-5545

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin ; PMLR 97:5546-5557

White-box vs Black-box: Bayes Optimal Strategies for Membership Inference

Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, Herve Jegou ; PMLR 97:5558-5567

Tractable n-Metrics for Multiple Graphs

Sam Safavi, Jose Bento ; PMLR 97:5568-5578

An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule

Touqir Sajed, Or Sheffet ; PMLR 97:5579-5588

Deep Gaussian Processes with Importance-Weighted Variational Inference

Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Deisenroth ; PMLR 97:5589-5598

Multivariate Submodular Optimization

Richard Santiago, F. Bruce Shepherd ; PMLR 97:5599-5609

Near optimal finite time identification of arbitrary linear dynamical systems

Tuhin Sarkar, Alexander Rakhlin ; PMLR 97:5610-5618

Breaking Inter-Layer Co-Adaptation by Classifier Anonymization

Ikuro Sato, Kohta Ishikawa, Guoqing Liu, Masayuki Tanaka ; PMLR 97:5619-5627

A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar ; PMLR 97:5628-5637

Locally Private Bayesian Inference for Count Models

Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna Wallach ; PMLR 97:5638-5648

Weakly-Supervised Temporal Localization via Occurrence Count Learning

Julien Schroeter, Kirill Sidorov, David Marshall ; PMLR 97:5649-5659

Discovering Context Effects from Raw Choice Data

Arjun Seshadri, Alex Peysakhovich, Johan Ugander ; PMLR 97:5660-5669

On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference

Rohin Shah, Noah Gundotra, Pieter Abbeel, Anca Dragan ; PMLR 97:5670-5679

Exploration Conscious Reinforcement Learning Revisited

Lior Shani, Yonathan Efroni, Shie Mannor ; PMLR 97:5680-5689

Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data

Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant ; PMLR 97:5690-5700

Conditional Independence in Testing Bayesian Networks

Yujia Shen, Haiying Huang, Arthur Choi, Adnan Darwiche ; PMLR 97:5701-5709

Learning to Clear the Market

Weiran Shen, Sebastien Lahaie, Renato Paes Leme ; PMLR 97:5710-5718

Mixture Models for Diverse Machine Translation: Tricks of the Trade

Tianxiao Shen, Myle Ott, Michael Auli, Marc’Aurelio Ranzato ; PMLR 97:5719-5728

Hessian Aided Policy Gradient

Zebang Shen, Alejandro Ribeiro, Hamed Hassani, Hui Qian, Chao Mi ; PMLR 97:5729-5738

Learning with Bad Training Data via Iterative Trimmed Loss Minimization

Yanyao Shen, Sujay Sanghavi ; PMLR 97:5739-5748

Replica Conditional Sequential Monte Carlo

Alex Shestopaloff, Arnaud Doucet ; PMLR 97:5749-5757

Scalable Training of Inference Networks for Gaussian-Process Models

Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu ; PMLR 97:5758-5768

Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning

Weishi Shi, Qi Yu ; PMLR 97:5769-5778

Model-Based Active Exploration

Pranav Shyam, Wojciech Jaśkowski, Faustino Gomez ; PMLR 97:5779-5788

Rehashing Kernel Evaluation in High Dimensions

Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Levis ; PMLR 97:5789-5798

Revisiting precision recall definition for generative modeling

Loic Simon, Ryan Webster, Julien Rabin ; PMLR 97:5799-5808

First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

Carl-Johann Simon-Gabriel, Yann Ollivier, Leon Bottou, Bernhard Schölkopf, David Lopez-Paz ; PMLR 97:5809-5817

Refined Complexity of PCA with Outliers

Kirill Simonov, Fedor Fomin, Petr Golovach, Fahad Panolan ; PMLR 97:5818-5826

A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks

Umut Simsekli, Levent Sagun, Mert Gurbuzbalaban ; PMLR 97:5827-5837

Non-Parametric Priors For Generative Adversarial Networks

Rajhans Singh, Pavan Turaga, Suren Jayasuriya, Ravi Garg, Martin Braun ; PMLR 97:5838-5847

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi ; PMLR 97:5848-5856

kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection

Lotfi Slim, Clément Chatelain, Chloe-Agathe Azencott, Jean-Philippe Vert ; PMLR 97:5857-5865

GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects

Edward Smith, Scott Fujimoto, Adriana Romero, David Meger ; PMLR 97:5866-5876

The Evolved Transformer

David So, Quoc Le, Chen Liang ; PMLR 97:5877-5886

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, Yung Yi ; PMLR 97:5887-5896

Distribution calibration for regression

Hao Song, Tom Diethe, Meelis Kull, Peter Flach ; PMLR 97:5897-5906

SELFIE: Refurbishing Unclean Samples for Robust Deep Learning

Hwanjun Song, Minseok Kim, Jae-Gil Lee ; PMLR 97:5907-5915

Revisiting the Softmax Bellman Operator: New Benefits and New Perspective

Zhao Song, Ron Parr, Lawrence Carin ; PMLR 97:5916-5925

MASS: Masked Sequence to Sequence Pre-training for Language Generation

Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu ; PMLR 97:5926-5936

Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication

Pedro Soto, Jun Li, Xiaodi Fan ; PMLR 97:5937-5945

Compressing Gradient Optimizers via Count-Sketches

Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava ; PMLR 97:5946-5955

Escaping Saddle Points with Adaptive Gradient Methods

Matthew Staib, Sashank Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra ; PMLR 97:5956-5965

Faster Attend-Infer-Repeat with Tractable Probabilistic Models

Karl Stelzner, Robert Peharz, Kristian Kersting ; PMLR 97:5966-5975

Insertion Transformer: Flexible Sequence Generation via Insertion Operations

Mitchell Stern, William Chan, Jamie Kiros, Jakob Uszkoreit ; PMLR 97:5976-5985

BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

Asa Cooper Stickland, Iain Murray ; PMLR 97:5986-5995

Learning Optimal Linear Regularizers

Matthew Streeter ; PMLR 97:5996-6004

CAB: Continuous Adaptive Blending for Policy Evaluation and Learning

Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims ; PMLR 97:6005-6014

Learning Distance for Sequences by Learning a Ground Metric

Bing Su, Ying Wu ; PMLR 97:6015-6025

Contextual Memory Trees

Wen Sun, Alina Beygelzimer, Hal Daumé Iii, John Langford, Paul Mineiro ; PMLR 97:6026-6035

Provably Efficient Imitation Learning from Observation Alone

Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell ; PMLR 97:6036-6045

Active Learning for Decision-Making from Imbalanced Observational Data

Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski ; PMLR 97:6046-6055

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

Raphael Suter, Djordje Miladinovic, Bernhard Schölkopf, Stefan Bauer ; PMLR 97:6056-6065

Hyperbolic Disk Embeddings for Directed Acyclic Graphs

Ryota Suzuki, Ryusuke Takahama, Shun Onoda ; PMLR 97:6066-6075

Accelerated Flow for Probability Distributions

Amirhossein Taghvaei, Prashant Mehta ; PMLR 97:6076-6085

Equivariant Transformer Networks

Kai Sheng Tai, Peter Bailis, Gregory Valiant ; PMLR 97:6086-6095

Making Deep Q-learning methods robust to time discretization

Corentin Tallec, Léonard Blier, Yann Ollivier ; PMLR 97:6096-6104

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Mingxing Tan, Quoc Le ; PMLR 97:6105-6114

Hierarchical Decompositional Mixtures of Variational Autoencoders

Ping Liang Tan, Robert Peharz ; PMLR 97:6115-6124

Mallows ranking models: maximum likelihood estimate and regeneration

Wenpin Tang ; PMLR 97:6125-6134

Correlated Variational Auto-Encoders

Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi ; PMLR 97:6135-6144

The Variational Predictive Natural Gradient

Da Tang, Rajesh Ranganath ; PMLR 97:6145-6154

DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression

Hanlin Tang, Chen Yu, Xiangru Lian, Tong Zhang, Ji Liu ; PMLR 97:6155-6165

Adaptive Neural Trees

Ryutaro Tanno, Kai Arulkumaran, Daniel Alexander, Antonio Criminisi, Aditya Nori ; PMLR 97:6166-6175

Variational Annealing of GANs: A Langevin Perspective

Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen, Chang Liu, Ruiyi Zhang, Georgiy Bobashev, Lawrence Carin Duke ; PMLR 97:6176-6185

Predicate Exchange: Inference with Declarative Knowledge

Zenna Tavares, Rajesh Ranganath, Javier Burroni, Armando Solar-Lezama, Edgar Minasyan ; PMLR 97:6186-6195

The Natural Language of Actions

Guy Tennenholtz, Shie Mannor ; PMLR 97:6196-6205

Kernel Normalized Cut: a Theoretical Revisit

Yoshikazu Terada, Michio Yamamoto ; PMLR 97:6206-6214

Action Robust Reinforcement Learning and Applications in Continuous Control

Chen Tessler, Yonathan Efroni, Shie Mannor ; PMLR 97:6215-6224

Concentration Inequalities for Conditional Value at Risk

Philip Thomas, Erik Learned-Miller ; PMLR 97:6225-6233

Combating Label Noise in Deep Learning using Abstention

Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof ; PMLR 97:6234-6243

ELF OpenGo: an analysis and open reimplementation of AlphaZero

Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick ; PMLR 97:6244-6253

Random Matrix Improved Covariance Estimation for a Large Class of Metrics

Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac ; PMLR 97:6254-6263

Transfer of Samples in Policy Search via Multiple Importance Sampling

Andrea Tirinzoni, Mattia Salvini, Marcello Restelli ; PMLR 97:6264-6274

Optimal Transport for structured data with application on graphs

Vayer Titouan, Nicolas Courty, Romain Tavenard, Chapel Laetitia, Rémi Flamary ; PMLR 97:6275-6284

Discovering Latent Covariance Structures for Multiple Time Series

Anh Tong, Jaesik Choi ; PMLR 97:6285-6294

Bayesian Generative Active Deep Learning

Toan Tran, Thanh-Toan Do, Ian Reid, Gustavo Carneiro ; PMLR 97:6295-6304

DeepNose: Using artificial neural networks to represent the space of odorants

Ngoc Tran, Daniel Kepple, Sergey Shuvaev, Alexei Koulakov ; PMLR 97:6305-6314

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations

Brian Trippe, Jonathan Huggins, Raj Agrawal, Tamara Broderick ; PMLR 97:6315-6324

Learning Hawkes Processes Under Synchronization Noise

William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran ; PMLR 97:6325-6334

Homomorphic Sensing

Manolis Tsakiris, Liangzu Peng ; PMLR 97:6335-6344

Metropolis-Hastings Generative Adversarial Networks

Ryan Turner, Jane Hung, Eric Frank, Yunus Saatchi, Jason Yosinski ; PMLR 97:6345-6353

Distributed, Egocentric Representations of Graphs for Detecting Critical Structures

Ruo-Chun Tzeng, Shan-Hung Wu ; PMLR 97:6354-6362

Sublinear Space Private Algorithms Under the Sliding Window Model

Jalaj Upadhyay ; PMLR 97:6363-6372

Fairness without Harm: Decoupled Classifiers with Preference Guarantees

Berk Ustun, Yang Liu, David Parkes ; PMLR 97:6373-6382

Large-Scale Sparse Kernel Canonical Correlation Analysis

Viivi Uurtio, Sahely Bhadra, Juho Rousu ; PMLR 97:6383-6391

Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD

Marten Van Dijk, Lam Nguyen, Phuong Ha Nguyen, Dzung Phan ; PMLR 97:6392-6400

Composing Value Functions in Reinforcement Learning

Benjamin Van Niekerk, Steven James, Adam Earle, Benjamin Rosman ; PMLR 97:6401-6409

Model Comparison for Semantic Grouping

Francisco Vargas, Kamen Brestnichki, Nils Hammerla ; PMLR 97:6410-6417

Learning Dependency Structures for Weak Supervision Models

Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Re ; PMLR 97:6418-6427

Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering

Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh ; PMLR 97:6428-6437

Manifold Mixup: Better Representations by Interpolating Hidden States

Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio ; PMLR 97:6438-6447

Maximum Likelihood Estimation for Learning Populations of Parameters

Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham Kakade ; PMLR 97:6448-6457

Understanding Priors in Bayesian Neural Networks at the Unit Level

Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo, Julyan Arbel ; PMLR 97:6458-6467

On the Design of Estimators for Bandit Off-Policy Evaluation

Nikos Vlassis, Aurelien Bibaut, Maria Dimakopoulou, Tony Jebara ; PMLR 97:6468-6476

Learning to select for a predefined ranking

Aleksei Ustimenko, Aleksandr Vorobev, Gleb Gusev, Pavel Serdyukov ; PMLR 97:6477-6486

On the Limitations of Representing Functions on Sets

Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, Michael A. Osborne ; PMLR 97:6487-6494

Graph Convolutional Gaussian Processes

Ian Walker, Ben Glocker ; PMLR 97:6495-6504

Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute

Tong Wang ; PMLR 97:6505-6514

Convolutional Poisson Gamma Belief Network

Chaojie Wang, Bo Chen, Sucheng Xiao, Mingyuan Zhou ; PMLR 97:6515-6525

Differentially Private Empirical Risk Minimization with Non-convex Loss Functions

Di Wang, Changyou Chen, Jinhui Xu ; PMLR 97:6526-6535

Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation

Ruohan Wang, Carlo Ciliberto, Pierluigi Vito Amadori, Yiannis Demiris ; PMLR 97:6536-6544

SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver

Po-Wei Wang, Priya Donti, Bryan Wilder, Zico Kolter ; PMLR 97:6545-6554

Improving Neural Language Modeling via Adversarial Training

Dilin Wang, Chengyue Gong, Qiang Liu ; PMLR 97:6555-6565

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

Chaoqi Wang, Roger Grosse, Sanja Fidler, Guodong Zhang ; PMLR 97:6566-6575

Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models

Dilin Wang, Qiang Liu ; PMLR 97:6576-6585

On the Convergence and Robustness of Adversarial Training

Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu ; PMLR 97:6586-6595

State-Regularized Recurrent Neural Networks

Cheng Wang, Mathias Niepert ; PMLR 97:6596-6606

Deep Factors for Forecasting

Yuyang Wang, Alex Smola, Danielle Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski ; PMLR 97:6607-6617

Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions

Hao Wang, Berk Ustun, Flavio Calmon ; PMLR 97:6618-6627

On Sparse Linear Regression in the Local Differential Privacy Model

Di Wang, Jinhui Xu ; PMLR 97:6628-6637

Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random

Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi ; PMLR 97:6638-6647

On the Generalization Gap in Reparameterizable Reinforcement Learning

Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher ; PMLR 97:6648-6658

Bias Also Matters: Bias Attribution for Deep Neural Network Explanation

Shengjie Wang, Tianyi Zhou, Jeff Bilmes ; PMLR 97:6659-6667

Jumpout : Improved Dropout for Deep Neural Networks with ReLUs

Shengjie Wang, Tianyi Zhou, Jeff Bilmes ; PMLR 97:6668-6676

AdaGrad Stepsizes: Sharp Convergence Over Nonconvex Landscapes

Rachel Ward, Xiaoxia Wu, Leon Bottou ; PMLR 97:6677-6686

Generalized Linear Rule Models

Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Gunluk ; PMLR 97:6687-6696

On the statistical rate of nonlinear recovery in generative models with heavy-tailed data

Xiaohan Wei, Zhuoran Yang, Zhaoran Wang ; PMLR 97:6697-6706

CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration

Gellert Weisz, Andras Gyorgy, Csaba Szepesvari ; PMLR 97:6707-6715

Non-Monotonic Sequential Text Generation

Sean Welleck, Kianté Brantley, Hal Daumé Iii, Kyunghyun Cho ; PMLR 97:6716-6726

PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach

Lily Weng, Pin-Yu Chen, Lam Nguyen, Mark Squillante, Akhilan Boopathy, Ivan Oseledets, Luca Daniel ; PMLR 97:6727-6736

Learning deep kernels for exponential family densities

Li Wenliang, Dougal Sutherland, Heiko Strathmann, Arthur Gretton ; PMLR 97:6737-6746

Improving Model Selection by Employing the Test Data

Max Westphal, Werner Brannath ; PMLR 97:6747-6756

Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth

Jacob Whitehill, Anand Ramakrishnan ; PMLR 97:6757-6765

Moment-Based Variational Inference for Markov Jump Processes

Christian Wildner, Heinz Koeppl ; PMLR 97:6766-6775

End-to-End Probabilistic Inference for Nonstationary Audio Analysis

William Wilkinson, Michael Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin ; PMLR 97:6776-6785

Fairness risk measures

Robert Williamson, Aditya Menon ; PMLR 97:6786-6797

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation

Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen ; PMLR 97:6798-6807

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations

Eric Wong, Frank Schmidt, Zico Kolter ; PMLR 97:6808-6817

Imitation Learning from Imperfect Demonstration

Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama ; PMLR 97:6818-6827

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling

Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar ; PMLR 97:6828-6839

Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin

Xi-Zhu Wu, Song Liu, Zhi-Hua Zhou ; PMLR 97:6840-6849

Deep Compressed Sensing

Yan Wu, Mihaela Rosca, Timothy Lillicrap ; PMLR 97:6850-6860

Simplifying Graph Convolutional Networks

Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, Kilian Weinberger ; PMLR 97:6861-6871

Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment

Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary Lipton ; PMLR 97:6872-6881

On Scalable and Efficient Computation of Large Scale Optimal Transport

Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha ; PMLR 97:6882-6892

Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance

Cong Xie, Sanmi Koyejo, Indranil Gupta ; PMLR 97:6893-6901

Differentiable Linearized ADMM

Xingyu Xie, Jianlong Wu, Guangcan Liu, Zhisheng Zhong, Zhouchen Lin ; PMLR 97:6902-6911

Calibrated Approximate Bayesian Inference

Hanwen Xing, Geoff Nicholls, Jeong Lee ; PMLR 97:6912-6920

Power k-Means Clustering

Jason Xu, Kenneth Lange ; PMLR 97:6921-6931

Gromov-Wasserstein Learning for Graph Matching and Node Embedding

Hongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin Duke ; PMLR 97:6932-6941

Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence

Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang ; PMLR 97:6942-6951

Learning a Prior over Intent via Meta-Inverse Reinforcement Learning

Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, Chelsea Finn ; PMLR 97:6952-6962

Variational Russian Roulette for Deep Bayesian Nonparametrics

Kai Xu, Akash Srivastava, Charles Sutton ; PMLR 97:6963-6972

Supervised Hierarchical Clustering with Exponential Linkage

Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew Mccallum ; PMLR 97:6973-6983

Learning to Prove Theorems via Interacting with Proof Assistants

Kaiyu Yang, Jia Deng ; PMLR 97:6984-6994

Sample-Optimal Parametric Q-Learning Using Linearly Additive Features

Lin Yang, Mengdi Wang ; PMLR 97:6995-7004

LegoNet: Efficient Convolutional Neural Networks with Lego Filters

Zhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu ; PMLR 97:7005-7014

SWALP : Stochastic Weight Averaging in Low Precision Training

Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Chris De Sa ; PMLR 97:7015-7024

ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

Yuzhe Yang, Guo Zhang, Dina Katabi, Zhi Xu ; PMLR 97:7025-7034

Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations

Quanming Yao, James Tin-Yau Kwok, Bo Han ; PMLR 97:7035-7044

Hierarchically Structured Meta-learning

Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li ; PMLR 97:7045-7054

Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering

Taisuke Yasuda, David Woodruff, Manuel Fernandez ; PMLR 97:7055-7063

Understanding Geometry of Encoder-Decoder CNNs

Jong Chul Ye, Woon Kyoung Sung ; PMLR 97:7064-7073

Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning

Dong Yin, Yudong Chen, Ramchandran Kannan, Peter Bartlett ; PMLR 97:7074-7084

Rademacher Complexity for Adversarially Robust Generalization

Dong Yin, Ramchandran Kannan, Peter Bartlett ; PMLR 97:7085-7094

ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables

Mingzhang Yin, Yuguang Yue, Mingyuan Zhou ; PMLR 97:7095-7104

NAS-Bench-101: Towards Reproducible Neural Architecture Search

Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter ; PMLR 97:7105-7114

TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning

Sung Whan Yoon, Jun Seo, Jaekyun Moon ; PMLR 97:7115-7123

Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation

Kaichao You, Ximei Wang, Mingsheng Long, Michael Jordan ; PMLR 97:7124-7133

Position-aware Graph Neural Networks

Jiaxuan You, Rex Ying, Jure Leskovec ; PMLR 97:7134-7143

Learning Neurosymbolic Generative Models via Program Synthesis

Halley Young, Osbert Bastani, Mayur Naik ; PMLR 97:7144-7153

DAG-GNN: DAG Structure Learning with Graph Neural Networks

Yue Yu, Jie Chen, Tian Gao, Mo Yu ; PMLR 97:7154-7163

How does Disagreement Help Generalization against Label Corruption?

Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, Masashi Sugiyama ; PMLR 97:7164-7173

On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization

Hao Yu, Rong Jin ; PMLR 97:7174-7183

On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization

Hao Yu, Rong Jin, Sen Yang ; PMLR 97:7184-7193

Multi-Agent Adversarial Inverse Reinforcement Learning

Lantao Yu, Jiaming Song, Stefano Ermon ; PMLR 97:7194-7201

Distributed Learning over Unreliable Networks

Chen Yu, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu ; PMLR 97:7202-7212

Online Adaptive Principal Component Analysis and Its extensions

Jianjun Yuan, Andrew Lamperski ; PMLR 97:7213-7221

Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation

Jinyang Yuan, Bin Li, Xiangyang Xue ; PMLR 97:7222-7231

Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory

Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, Qingyun Sun ; PMLR 97:7232-7241

Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning

Jihun Yun, Peng Zheng, Eunho Yang, Aurelie Lozano, Aleksandr Aravkin ; PMLR 97:7242-7251

Bayesian Nonparametric Federated Learning of Neural Networks

Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, Yasaman Khazaeni ; PMLR 97:7252-7261

Dirichlet Simplex Nest and Geometric Inference

Mikhail Yurochkin, Aritra Guha, Yuekai Sun, Xuanlong Nguyen ; PMLR 97:7262-7271

A Conditional-Gradient-Based Augmented Lagrangian Framework

Alp Yurtsever, Olivier Fercoq, Volkan Cevher ; PMLR 97:7272-7281

Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator

Alp Yurtsever, Suvrit Sra, Volkan Cevher ; PMLR 97:7282-7291

Context-Aware Zero-Shot Learning for Object Recognition

Eloi Zablocki, Patrick Bordes, Laure Soulier, Benjamin Piwowarski, Patrick Gallinari ; PMLR 97:7292-7303

Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds

Andrea Zanette, Emma Brunskill ; PMLR 97:7304-7312

Global Convergence of Block Coordinate Descent in Deep Learning

Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao ; PMLR 97:7313-7323

Making Convolutional Networks Shift-Invariant Again

Richard Zhang ; PMLR 97:7324-7334

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Chicheng Zhang, Alekh Agarwal, Hal Daumé Iii, John Langford, Sahand Negahban ; PMLR 97:7335-7344

When Samples Are Strategically Selected

Hanrui Zhang, Yu Cheng, Vincent Conitzer ; PMLR 97:7345-7353

Self-Attention Generative Adversarial Networks

Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena ; PMLR 97:7354-7363

Circuit-GNN: Graph Neural Networks for Distributed Circuit Design

Guo Zhang, Hao He, Dina Katabi ; PMLR 97:7364-7373

LatentGNN: Learning Efficient Non-local Relations for Visual Recognition

Songyang Zhang, Xuming He, Shipeng Yan ; PMLR 97:7374-7383

Neural Collaborative Subspace Clustering

Tong Zhang, Pan Ji, Mehrtash Harandi, Wenbing Huang, Hongdong Li ; PMLR 97:7384-7393

Incremental Randomized Sketching for Online Kernel Learning

Xiao Zhang, Shizhong Liao ; PMLR 97:7394-7403

Bridging Theory and Algorithm for Domain Adaptation

Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael Jordan ; PMLR 97:7404-7413

Adaptive Regret of Convex and Smooth Functions

Lijun Zhang, Tie-Yan Liu, Zhi-Hua Zhou ; PMLR 97:7414-7423

Random Function Priors for Correlation Modeling

Aonan Zhang, John Paisley ; PMLR 97:7424-7433

Co-Representation Network for Generalized Zero-Shot Learning

Fei Zhang, Guangming Shi ; PMLR 97:7434-7443

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning

Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, Sergey Levine ; PMLR 97:7444-7453

A Composite Randomized Incremental Gradient Method

Junyu Zhang, Lin Xiao ; PMLR 97:7454-7462

Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models

Chenyang Zhang, Guosheng Yin ; PMLR 97:7463-7471

Theoretically Principled Trade-off between Robustness and Accuracy

Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, Michael Jordan ; PMLR 97:7472-7482

Learning Novel Policies For Tasks

Yunbo Zhang, Wenhao Yu, Greg Turk ; PMLR 97:7483-7492

Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization

Kai Zhang, Sheng Zhang, Jun Liu, Jun Wang, Jie Zhang ; PMLR 97:7493-7501

Interpreting Adversarially Trained Convolutional Neural Networks

Tianyuan Zhang, Zhanxing Zhu ; PMLR 97:7502-7511

Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits

Martin Zhang, James Zou, David Tse ; PMLR 97:7512-7522

On Learning Invariant Representations for Domain Adaptation

Han Zhao, Remi Tachet Des Combes, Kun Zhang, Geoffrey Gordon ; PMLR 97:7523-7532

Metric-Optimized Example Weights

Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya Gupta ; PMLR 97:7533-7542

Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, Zhiru Zhang ; PMLR 97:7543-7552

Maximum Entropy-Regularized Multi-Goal Reinforcement Learning

Rui Zhao, Xudong Sun, Volker Tresp ; PMLR 97:7553-7562

Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization

Baojian Zhou, Feng Chen, Yiming Ying ; PMLR 97:7563-7573

Lower Bounds for Smooth Nonconvex Finite-Sum Optimization

Dongruo Zhou, Quanquan Gu ; PMLR 97:7574-7583

Lipschitz Generative Adversarial Nets

Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang ; PMLR 97:7584-7593

Toward Understanding the Importance of Noise in Training Neural Networks

Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao ; PMLR 97:7594-7602

BayesNAS: A Bayesian Approach for Neural Architecture Search

Hongpeng Zhou, Minghao Yang, Jun Wang, Wei Pan ; PMLR 97:7603-7613

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets

Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein ; PMLR 97:7614-7623

Improved Dynamic Graph Learning through Fault-Tolerant Sparsification

Chunjiang Zhu, Sabine Storandt, Kam-Yiu Lam, Song Han, Jinbo Bi ; PMLR 97:7624-7633

Poission Subsampled Rényi Differential Privacy

Yuqing Zhu, Yu-Xiang Wang ; PMLR 97:7634-7642

Learning Classifiers for Target Domain with Limited or No Labels

Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama ; PMLR 97:7643-7653

The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects

Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma ; PMLR 97:7654-7663

Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization

Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona ; PMLR 97:7664-7672

Latent Normalizing Flows for Discrete Sequences

Zachary Ziegler, Alexander Rush ; PMLR 97:7673-7682

Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously

Julian Zimmert, Haipeng Luo, Chen-Yu Wei ; PMLR 97:7683-7692

Fast Context Adaptation via Meta-Learning

Luisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, Shimon Whiteson ; PMLR 97:7693-7702

Natural Analysts in Adaptive Data Analysis

Tijana Zrnic, Moritz Hardt ; PMLR 97:7703-7711

subscribe via RSS

This site last compiled Thu, 13 Jun 2019 22:26:29 +0000
Github Account Copyright © PMLR 2019. All rights reserved.