Volume 80: International Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden

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Editors: Jennifer Dy, Andreas Krause

[bib][citeproc]

Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems

Marc Abeille, Alessandro Lazaric ; PMLR 80:1-9

State Abstractions for Lifelong Reinforcement Learning

David Abel, Dilip Arumugam, Lucas Lehnert, Michael Littman ; PMLR 80:10-19

Policy and Value Transfer in Lifelong Reinforcement Learning

David Abel, Yuu Jinnai, Sophie Yue Guo, George Konidaris, Michael Littman ; PMLR 80:20-29

INSPECTRE: Privately Estimating the Unseen

Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang ; PMLR 80:30-39

Learning Representations and Generative Models for 3D Point Clouds

Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas ; PMLR 80:40-49

Discovering Interpretable Representations for Both Deep Generative and Discriminative Models

Tameem Adel, Zoubin Ghahramani, Adrian Weller ; PMLR 80:50-59

A Reductions Approach to Fair Classification

Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach ; PMLR 80:60-69

Accelerated Spectral Ranking

Arpit Agarwal, Prathamesh Patil, Shivani Agarwal ; PMLR 80:70-79

MISSION: Ultra Large-Scale Feature Selection using Count-Sketches

Amirali Aghazadeh, Ryan Spring, Daniel Lejeune, Gautam Dasarathy, Anshumali Shrivastava, baraniuk ; PMLR 80:80-88

Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models

Raj Agrawal, Caroline Uhler, Tamara Broderick ; PMLR 80:89-98

Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy

Shipra Agrawal, Morteza Zadimoghaddam, Vahab Mirrokni ; PMLR 80:99-108

Bucket Renormalization for Approximate Inference

Sungsoo Ahn, Michael Chertkov, Adrian Weller, Jinwoo Shin ; PMLR 80:109-118

oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis

Samuel K. Ainsworth, Nicholas J. Foti, Adrian K. C. Lee, Emily B. Fox ; PMLR 80:119-128

Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design

Ahmed Alaa, Mihaela Schaar ; PMLR 80:129-138

AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning

Ahmed Alaa, Mihaela Schaar ; PMLR 80:139-148

Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization

Ibrahim Alabdulmohsin ; PMLR 80:149-158

Fixing a Broken ELBO

Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif A. Saurous, Kevin Murphy ; PMLR 80:159-168

Differentially Private Identity and Equivalence Testing of Discrete Distributions

Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld ; PMLR 80:169-178

Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization

Zeyuan Allen-Zhu ; PMLR 80:179-185

Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits

Zeyuan Allen-Zhu, Sebastien Bubeck, Yuanzhi Li ; PMLR 80:186-194

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data

Amjad Almahairi, Sai Rajeshwar, Alessandro Sordoni, Philip Bachman, Aaron Courville ; PMLR 80:195-204

Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory

Ron Amit, Ron Meir ; PMLR 80:205-214

MAGAN: Aligning Biological Manifolds

Matthew Amodio, Smita Krishnaswamy ; PMLR 80:215-223

Subspace Embedding and Linear Regression with Orlicz Norm

Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong ; PMLR 80:224-233

Efficient Gradient-Free Variational Inference using Policy Search

Oleg Arenz, Mingjun Zhong, Gerhard Neumann ; PMLR 80:234-243

On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization

Sanjeev Arora, Nadav Cohen, Elad Hazan ; PMLR 80:244-253

Stronger Generalization Bounds for Deep Nets via a Compression Approach

Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang ; PMLR 80:254-263

Lipschitz Continuity in Model-based Reinforcement Learning

Kavosh Asadi, Dipendra Misra, Michael Littman ; PMLR 80:264-273

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

Anish Athalye, Nicholas Carlini, David Wagner ; PMLR 80:274-283

Synthesizing Robust Adversarial Examples

Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok ; PMLR 80:284-293

Clustering Semi-Random Mixtures of Gaussians

Pranjal Awasthi, Aravindan Vijayaraghavan ; PMLR 80:294-303

Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing

Davide Bacciu, Federico Errica, Alessio Micheli ; PMLR 80:304-313

Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions

Wenruo Bai, Jeffrey Bilmes ; PMLR 80:314-323

Comparing Dynamics: Deep Neural Networks versus Glassy Systems

Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gerard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli ; PMLR 80:324-333

SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions

Chandrajit Bajaj, Tingran Gao, Zihang He, Qixing Huang, Zhenxiao Liang ; PMLR 80:334-343

A Boo(n) for Evaluating Architecture Performance

Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst ; PMLR 80:344-352

Learning to Branch

Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik ; PMLR 80:353-362

The Mechanics of n-Player Differentiable Games

David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel ; PMLR 80:363-372

Spline Filters For End-to-End Deep Learning

Randall Balestriero, Romain Cosentino, Herve Glotin, Richard Baraniuk ; PMLR 80:373-382

A Spline Theory of Deep Networks

Randall Balestriero, baraniuk ; PMLR 80:383-392

Approximation Guarantees for Adaptive Sampling

Eric Balkanski, Yaron Singer ; PMLR 80:393-402

Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising

Borja Balle, Yu-Xiang Wang ; PMLR 80:403-412

Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients

Lukas Balles, Philipp Hennig ; PMLR 80:413-422

Differentially Private Database Release via Kernel Mean Embeddings

Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf ; PMLR 80:423-431

Improving Optimization in Models With Continuous Symmetry Breaking

Robert Bamler, Stephan Mandt ; PMLR 80:432-441

Improved Training of Generative Adversarial Networks using Representative Features

Duhyeon Bang, Hyunjung Shim ; PMLR 80:442-451

Using Inherent Structures to design Lean 2-layer RBMs

Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya ; PMLR 80:452-460

Classification from Pairwise Similarity and Unlabeled Data

Han Bao, Gang Niu, Masashi Sugiyama ; PMLR 80:461-470

Bayesian Optimization of Combinatorial Structures

Ricardo Baptista, Matthias Poloczek ; PMLR 80:471-480

Geodesic Convolutional Shape Optimization

Pierre Baque, Edoardo Remelli, Francois Fleuret, Pascal Fua ; PMLR 80:481-490

Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems

Eugenio Bargiacchi, Timothy Verstraeten, Diederik Roijers, Ann Nowé, Hado Hasselt ; PMLR 80:491-499

Testing Sparsity over Known and Unknown Bases

Siddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal ; PMLR 80:500-509

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement

Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, Remi Munos ; PMLR 80:510-519

Gradient descent with identity initialization efficiently learns positive definite linear transformations

Peter Bartlett, Dave Helmbold, Phil Long ; PMLR 80:520-529

Mutual Information Neural Estimation

Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Devon Hjelm, Aaron Courville ; PMLR 80:530-539

To Understand Deep Learning We Need to Understand Kernel Learning

Mikhail Belkin, Siyuan Ma, Soumik Mandal ; PMLR 80:540-548

Understanding and Simplifying One-Shot Architecture Search

Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc Le ; PMLR 80:549-558

SIGNSGD: Compressed Optimisation for Non-Convex Problems

Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar ; PMLR 80:559-568

Distributed Clustering via LSH Based Data Partitioning

Aditya Bhaskara, Maheshakya Wijewardena ; PMLR 80:569-578

Autoregressive Convolutional Neural Networks for Asynchronous Time Series

Mikolaj Binkowski, Gautier Marti, Philippe Donnat ; PMLR 80:579-588

Adaptive Sampled Softmax with Kernel Based Sampling

Guy Blanc, Steffen Rendle ; PMLR 80:589-598

Optimizing the Latent Space of Generative Networks

Piotr Bojanowski, Armand Joulin, David Lopez-Pas, Arthur Szlam ; PMLR 80:599-608

NetGAN: Generating Graphs via Random Walks

Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann ; PMLR 80:609-618

A Progressive Batching L-BFGS Method for Machine Learning

Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang ; PMLR 80:619-628

Prediction Rule Reshaping

Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty ; PMLR 80:629-637

QuantTree: Histograms for Change Detection in Multivariate Data Streams

Giacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Macciò ; PMLR 80:638-647

Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order

Vladimir Braverman, Stephen Chestnut, Robert Krauthgamer, Yi Li, David Woodruff, Lin Yang ; PMLR 80:648-657

Predict and Constrain: Modeling Cardinality in Deep Structured Prediction

Nataly Brukhim, Amir Globerson ; PMLR 80:658-666

Quasi-Monte Carlo Variational Inference

Alexander Buchholz, Florian Wenzel, Stephan Mandt ; PMLR 80:667-676

Path-Level Network Transformation for Efficient Architecture Search

Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu ; PMLR 80:677-686

Improved Large-Scale Graph Learning through Ridge Spectral Sparsification

Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko ; PMLR 80:687-696

Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent

Trevor Campbell, Tamara Broderick ; PMLR 80:697-705

Adversarial Learning with Local Coordinate Coding

Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan ; PMLR 80:706-714

Fair and Diverse DPP-Based Data Summarization

Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth Vishnoi ; PMLR 80:715-724

Conditional Noise-Contrastive Estimation of Unnormalised Models

Ciwan Ceylan, Michael U. Gutmann ; PMLR 80:725-733

Adversarial Time-to-Event Modeling

Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein, Lawrence Carin, Ricardo Henao ; PMLR 80:734-743

Stability and Generalization of Learning Algorithms that Converge to Global Optima

Zachary Charles, Dimitris Papailiopoulos ; PMLR 80:744-753

Learning and Memorization

Satrajit Chatterjee ; PMLR 80:754-762

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo

Niladri Chatterji, Nicolas Flammarion, Yian Ma, Peter Bartlett, Michael Jordan ; PMLR 80:763-772

Hierarchical Clustering with Structural Constraints

Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar ; PMLR 80:773-782

Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series

Zhengping Che, Sanjay Purushotham, Guangyu Li, Bo Jiang, Yan Liu ; PMLR 80:783-792

GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks

Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, Andrew Rabinovich ; PMLR 80:793-802

Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?

Lin Chen, Moran Feldman, Amin Karbasi ; PMLR 80:803-812

Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity

Lin Chen, Christopher Harshaw, Hamed Hassani, Amin Karbasi ; PMLR 80:813-822

Continuous-Time Flows for Efficient Inference and Density Estimation

Changyou Chen, Chunyuan Li, Liquan Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin ; PMLR 80:823-832

Scalable Bilinear Learning Using State and Action Features

Yichen Chen, Lihong Li, Mengdi Wang ; PMLR 80:833-842

Stein Points

Wilson Chen, Lester Mackey, Jackson Gorham, Francois-Xavier Briol, Chris Oates ; PMLR 80:843-852

Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations

Ting Chen, Martin Renqiang Min, Yizhou Sun ; PMLR 80:853-862

PixelSNAIL: An Improved Autoregressive Generative Model

Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel ; PMLR 80:863-871

Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks

Minmin Chen, Jeffrey Pennington, Samuel Schoenholz ; PMLR 80:872-881

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

Jianbo Chen, Le Song, Martin Wainwright, Michael Jordan ; PMLR 80:882-891

Variational Inference and Model Selection with Generalized Evidence Bounds

Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, Lawrence Carin ; PMLR 80:892-901

DRACO: Byzantine-resilient Distributed Training via Redundant Gradients

Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos ; PMLR 80:902-911

SADAGRAD: Strongly Adaptive Stochastic Gradient Methods

Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang ; PMLR 80:912-920

Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization

Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu ; PMLR 80:921-930

End-to-End Learning for the Deep Multivariate Probit Model

Di Chen, Yexiang Xue, Carla Gomes ; PMLR 80:931-940

Stochastic Training of Graph Convolutional Networks with Variance Reduction

Jianfei Chen, Jun Zhu, Le Song ; PMLR 80:941-949

Extreme Learning to Rank via Low Rank Assumption

Minhao Cheng, Ian Davidson, Cho-Jui Hsieh ; PMLR 80:950-959

Learning a Mixture of Two Multinomial Logits

Flavio Chierichetti, Ravi Kumar, Andrew Tomkins ; PMLR 80:960-968

Structured Evolution with Compact Architectures for Scalable Policy Optimization

Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller ; PMLR 80:969-977

Path Consistency Learning in Tsallis Entropy Regularized MDPs

Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh ; PMLR 80:978-987

An Iterative, Sketching-based Framework for Ridge Regression

Agniva Chowdhury, Jiasen Yang, Petros Drineas ; PMLR 80:988-997

Stochastic Wasserstein Barycenters

Sebastian Claici, Edward Chien, Justin Solomon ; PMLR 80:998-1007

Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings

John Co-Reyes, YuXuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine ; PMLR 80:1008-1017

On Acceleration with Noise-Corrupted Gradients

Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia ; PMLR 80:1018-1027

Online Linear Quadratic Control

Alon Cohen, Avinatan Hasidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar ; PMLR 80:1028-1037

GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms

Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer ; PMLR 80:1038-1047

Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski p-Norms

Graham Cormode, Charlie Dickens, David Woodruff ; PMLR 80:1048-1056

Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation

Dane Corneil, Wulfram Gerstner, Johanni Brea ; PMLR 80:1057-1066

Online Learning with Abstention

Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang ; PMLR 80:1067-1075

Constrained Interacting Submodular Groupings

Andrew Cotter, Mahdi Milani Fard, Seungil You, Maya Gupta, Jeff Bilmes ; PMLR 80:1076-1085

Inference Suboptimality in Variational Autoencoders

Chris Cremer, Xuechen Li, David Duvenaud ; PMLR 80:1086-1094

Mix & Match Agent Curricula for Reinforcement Learning

Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu ; PMLR 80:1095-1103

Implicit Quantile Networks for Distributional Reinforcement Learning

Will Dabney, Georg Ostrovski, David Silver, Remi Munos ; PMLR 80:1104-1113

Learning Steady-States of Iterative Algorithms over Graphs

Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song ; PMLR 80:1114-1122

Adversarial Attack on Graph Structured Data

Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song ; PMLR 80:1123-1132

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation

Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song ; PMLR 80:1133-1142

Compressing Neural Networks using the Variational Information Bottleneck

Bin Dai, Chen Zhu, Baining Guo, David Wipf ; PMLR 80:1143-1152

Asynchronous Byzantine Machine Learning (the case of SGD)

Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Rhicheek Patra, Mahsa Taziki ; PMLR 80:1153-1162

Escaping Saddles with Stochastic Gradients

Hadi Daneshmand, Jonas Kohler, Aurelien Lucchi, Thomas Hofmann ; PMLR 80:1163-1172

Minibatch Gibbs Sampling on Large Graphical Models

Christopher De Sa, Vincent Chen, Wing Wong ; PMLR 80:1173-1181

Stochastic Video Generation with a Learned Prior

Emily Denton, Rob Fergus ; PMLR 80:1182-1191

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez, Steffen Udluft ; PMLR 80:1192-1201

Accurate Inference for Adaptive Linear Models

Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy ; PMLR 80:1202-1211

Variational Network Inference: Strong and Stable with Concrete Support

Amir Dezfouli, Edwin V. Bonilla, Richard Nock ; PMLR 80:1212-1221

Modeling Sparse Deviations for Compressed Sensing using Generative Models

Manik Dhar, Aditya Grover, Stefano Ermon ; PMLR 80:1222-1231

Alternating Randomized Block Coordinate Descent

Jelena Diakonikolas, Lorenzo Orecchia ; PMLR 80:1232-1240

Learning to Act in Decentralized Partially Observable MDPs

Jilles Dibangoye, Olivier Buffet ; PMLR 80:1241-1250

Noisin: Unbiased Regularization for Recurrent Neural Networks

Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David Blei ; PMLR 80:1251-1260

Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning

Thomas Dietterich, George Trimponias, Zhitang Chen ; PMLR 80:1261-1269

Coordinated Exploration in Concurrent Reinforcement Learning

Maria Dimakopoulou, Benjamin Van Roy ; PMLR 80:1270-1278

Probabilistic Recurrent State-Space Models

Andreas Doerr, Christian Daniel, Martin Schiegg, Duy Nguyen-Tuong, Stefan Schaal, Marc Toussaint, Sebastian Trimpe ; PMLR 80:1279-1288

Randomized Block Cubic Newton Method

Nikita Doikov, Peter Richtarik ; PMLR 80:1289-1297

Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering

Ahmed Douik, Babak Hassibi ; PMLR 80:1298-1307

Essentially No Barriers in Neural Network Energy Landscape

Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred Hamprecht ; PMLR 80:1308-1317

Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer

Alexey Drutsa ; PMLR 80:1318-1327

On the Power of Over-parametrization in Neural Networks with Quadratic Activation

Simon S. Du, Jason D. Lee ; PMLR 80:1328-1337

Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima

Simon S. Du, Jason D. Lee, Yuandong Tian, Aarti Singh, Barnabas Poczos ; PMLR 80:1338-1347

Investigating Human Priors for Playing Video Games

Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei Efros ; PMLR 80:1348-1356

A Distributed Second-Order Algorithm You Can Trust

Celestine Duenner, Aurelien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi ; PMLR 80:1357-1365

Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn’s Algorithm

Pavel Dvurechensky, Alexander Gasnikov, Alexey Kroshnin ; PMLR 80:1366-1375

Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors

Gintare Karolina Dziugaite, Daniel Roy ; PMLR 80:1376-1385

Beyond the One-Step Greedy Approach in Reinforcement Learning

Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor ; PMLR 80:1386-1395

Parallel and Streaming Algorithms for K-Core Decomposition

Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni ; PMLR 80:1396-1405

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu ; PMLR 80:1406-1415

Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)

Trefor Evans, Prasanth Nair ; PMLR 80:1416-1425

The Limits of Maxing, Ranking, and Preference Learning

Moein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar ; PMLR 80:1426-1435

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

Stefan Falkner, Aaron Klein, Frank Hutter ; PMLR 80:1436-1445

More Robust Doubly Robust Off-policy Evaluation

Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh ; PMLR 80:1446-1455

Efficient and Consistent Adversarial Bipartite Matching

Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian Ziebart ; PMLR 80:1456-1465

Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator

Maryam Fazel, Rong Ge, Sham Kakade, Mehran Mesbahi ; PMLR 80:1466-1475

CRVI: Convex Relaxation for Variational Inference

Ghazal Fazelnia, John Paisley ; PMLR 80:1476-1484

Fourier Policy Gradients

Matthew Fellows, Kamil Ciosek, Shimon Whiteson ; PMLR 80:1485-1494

Nonparametric variable importance using an augmented neural network with multi-task learning

Jean Feng, Brian D. Williamson, Marco Carone, Noah Simon ; PMLR 80:1495-1504

Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization

Louis Filstroff, Alberto Lumbreras, Cédric Févotte ; PMLR 80:1505-1513

Automatic Goal Generation for Reinforcement Learning Agents

Carlos Florensa, David Held, Xinyang Geng, Pieter Abbeel ; PMLR 80:1514-1523

DiCE: The Infinitely Differentiable Monte Carlo Estimator

Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric Xing, Shimon Whiteson ; PMLR 80:1524-1533

Practical Contextual Bandits with Regression Oracles

Dylan Foster, Alekh Agarwal, Miroslav Dudik, Haipeng Luo, Robert Schapire ; PMLR 80:1534-1543

Generative Temporal Models with Spatial Memory for Partially Observed Environments

Marco Fraccaro, Danilo Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola ; PMLR 80:1544-1553

ADMM and Accelerated ADMM as Continuous Dynamical Systems

Guilherme Franca, Daniel Robinson, Rene Vidal ; PMLR 80:1554-1562

Bilevel Programming for Hyperparameter Optimization and Meta-Learning

Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimiliano Pontil ; PMLR 80:1563-1572

Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning

Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner ; PMLR 80:1573-1581

Addressing Function Approximation Error in Actor-Critic Methods

Scott Fujimoto, Herke Hoof, David Meger ; PMLR 80:1582-1591

Clipped Action Policy Gradient

Yasuhiro Fujita, Shin-ichi Maeda ; PMLR 80:1592-1601

Born-Again Neural Networks

Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar ; PMLR 80:1602-1611

Local Private Hypothesis Testing: Chi-Square Tests

Marco Gaboardi, Ryan Rogers ; PMLR 80:1612-1621

Inductive Two-layer Modeling with Parametric Bregman Transfer

Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu ; PMLR 80:1622-1631

Hyperbolic Entailment Cones for Learning Hierarchical Embeddings

Octavian-Eugen Ganea, Gary Becigneul, Thomas Hofmann ; PMLR 80:1632-1641

Parameterized Algorithms for the Matrix Completion Problem

Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider ; PMLR 80:1642-1651

Synthesizing Programs for Images using Reinforced Adversarial Learning

Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S. M. Ali Eslami, Oriol Vinyals ; PMLR 80:1652-1661

Spotlight: Optimizing Device Placement for Training Deep Neural Networks

Yuanxiang Gao, Li Chen, Baochun Li ; PMLR 80:1662-1670

Parallel Bayesian Network Structure Learning

Tian Gao, Dennis Wei ; PMLR 80:1671-1680

Structured Output Learning with Abstention: Application to Accurate Opinion Prediction

Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d’Alche-Buc ; PMLR 80:1681-1689

Conditional Neural Processes

Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, S. M. Ali Eslami ; PMLR 80:1690-1699

Temporal Poisson Square Root Graphical Models

Sinong Geng, Zhaobin Kuang, Peggy Peissig, David Page ; PMLR 80:1700-1709

The Generalization Error of Dictionary Learning with Moreau Envelopes

Alexandros Georgogiannis ; PMLR 80:1710-1718

Budgeted Experiment Design for Causal Structure Learning

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim ; PMLR 80:1719-1728

Linear Spectral Estimators and an Application to Phase Retrieval

Ramina Ghods, Andrew Lan, Tom Goldstein, Christoph Studer ; PMLR 80:1729-1738

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez ; PMLR 80:1739-1748

Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time

Asish Ghoshal, Jean Honorio ; PMLR 80:1749-1757

Robust and Scalable Models of Microbiome Dynamics

Travis Gibson, Georg Gerber ; PMLR 80:1758-1767

Non-Linear Motor Control by Local Learning in Spiking Neural Networks

Aditya Gilra, Wulfram Gerstner ; PMLR 80:1768-1777

Learning One Convolutional Layer with Overlapping Patches

Surbhi Goel, Adam Klivans, Raghu Meka ; PMLR 80:1778-1786

Visualizing and Understanding Atari Agents

Samuel Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern ; PMLR 80:1787-1796

Learning Policy Representations in Multiagent Systems

Aditya Grover, Maruan Al-Shedivat, Jayesh Gupta, Yuri Burda, Harrison Edwards ; PMLR 80:1797-1806

Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines

Bin Gu, Zhouyuan Huo, Cheng Deng, Heng Huang ; PMLR 80:1807-1816

Learning to Search with MCTSnets

Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Remi Munos, David Silver ; PMLR 80:1817-1826

Characterizing Implicit Bias in Terms of Optimization Geometry

Suriya Gunasekar, Jason Lee, Daniel Soudry, Nathan Srebro ; PMLR 80:1827-1836

Shampoo: Preconditioned Stochastic Tensor Optimization

Vineet Gupta, Tomer Koren, Yoram Singer ; PMLR 80:1837-1845

Latent Space Policies for Hierarchical Reinforcement Learning

Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine ; PMLR 80:1846-1855

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine ; PMLR 80:1856-1865

Comparison-Based Random Forests

Siavash Haghiri, Damien Garreau, Ulrike Luxburg ; PMLR 80:1866-1875

K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning

Jihun Hamm, Yung-Kyun Noh ; PMLR 80:1876-1884

Candidates vs. Noises Estimation for Large Multi-Class Classification Problem

Lei Han, Yiheng Huang, Tong Zhang ; PMLR 80:1885-1894

Stein Variational Gradient Descent Without Gradient

Jun Han, Qiang Liu ; PMLR 80:1895-1903

Rectify Heterogeneous Models with Semantic Mapping

Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou ; PMLR 80:1904-1913

Deep Models of Interactions Across Sets

Jason Hartford, Devon Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh ; PMLR 80:1914-1923

Learning Memory Access Patterns

Milad Hashemi, Kevin Swersky, Jamie Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan ; PMLR 80:1924-1933

Fairness Without Demographics in Repeated Loss Minimization

Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang ; PMLR 80:1934-1943

Multicalibration: Calibration for the (Computationally-Identifiable) Masses

Ursula Hebert-Johnson, Michael Kim, Omer Reingold, Guy Rothblum ; PMLR 80:1944-1953

Recurrent Predictive State Policy Networks

Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha Srinivasa, Geoffrey Gordon ; PMLR 80:1954-1963

Learning unknown ODE models with Gaussian processes

Markus Heinonen, Cagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki ; PMLR 80:1964-1973

Orthogonal Recurrent Neural Networks with Scaled Cayley Transform

Kyle Helfrich, Devin Willmott, Qiang Ye ; PMLR 80:1974-1983

Fast Bellman Updates for Robust MDPs

Chin Pang Ho, Marek Petrik, Wolfram Wiesemann ; PMLR 80:1984-1993

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, Trevor Darrell ; PMLR 80:1994-2003

Sound Abstraction and Decomposition of Probabilistic Programs

Steven Holtzen, Guy Broeck, Todd Millstein ; PMLR 80:2004-2013

Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks

Mingyi Hong, Meisam Razaviyayn, Jason Lee ; PMLR 80:2014-2023

Variational Bayesian dropout: pitfalls and fixes

Jiri Hron, Alexander G. G. Matthews, Zoubin Ghahramani ; PMLR 80:2024-2033

Does Distributionally Robust Supervised Learning Give Robust Classifiers?

Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama ; PMLR 80:2034-2042

Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs

Bin Hu, Stephen Wright, Laurent Lessard ; PMLR 80:2043-2052

Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices

Zengfeng Huang ; PMLR 80:2053-2062

Learning Deep ResNet Blocks Sequentially using Boosting Theory

Furong Huang, Jordan Ash, John Langford, Robert Schapire ; PMLR 80:2063-2072

Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling

Kejun Huang, Xiao Fu, Nicholas Sidiropoulos ; PMLR 80:2073-2082

Neural Autoregressive Flows

Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville ; PMLR 80:2083-2092

Topological Mixture Estimation

Steve Huntsman ; PMLR 80:2093-2102

Decoupled Parallel Backpropagation with Convergence Guarantee

Zhouyuan Huo, Bin Gu, Yang, Heng Huang ; PMLR 80:2103-2111

Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning

Rodrigo Toro Icarte, Toryn Klassen, Richard Valenzano, Sheila McIlraith ; PMLR 80:2112-2121

Deep Variational Reinforcement Learning for POMDPs

Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson ; PMLR 80:2122-2131

Attention-based Deep Multiple Instance Learning

Maximilian Ilse, Jakub Tomczak, Max Welling ; PMLR 80:2132-2141

Black-box Adversarial Attacks with Limited Queries and Information

Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin ; PMLR 80:2142-2151

Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model

Hideaki Imamura, Issei Sato, Masashi Sugiyama ; PMLR 80:2152-2161

Improving Regression Performance with Distributional Losses

Ehsan Imani, Martha White ; PMLR 80:2162-2171

Deep Density Destructors

David Inouye, Pradeep Ravikumar ; PMLR 80:2172-2180

Unbiased Objective Estimation in Predictive Optimization

Shinji Ito, Akihiro Yabe, Ryohei Fujimaki ; PMLR 80:2181-2190

Anonymous Walk Embeddings

Sergey Ivanov, Evgeny Burnaev ; PMLR 80:2191-2200

Learning Binary Latent Variable Models: A Tensor Eigenpair Approach

Ariel Jaffe, Roi Weiss, Shai Carmi, Yuval Kluger, Boaz Nadler ; PMLR 80:2201-2210

Firing Bandits: Optimizing Crowdfunding

Lalit Jain, Kevin Jamieson ; PMLR 80:2211-2219

Differentially Private Matrix Completion Revisited

Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta ; PMLR 80:2220-2229

Video Prediction with Appearance and Motion Conditions

Yunseok Jang, Gunhee Kim, Yale Song ; PMLR 80:2230-2239

Pathwise Derivatives Beyond the Reparameterization Trick

Martin Jankowiak, Fritz Obermeyer ; PMLR 80:2240-2249

Detecting non-causal artifacts in multivariate linear regression models

Dominik Janzing, Bernhard Schölkopf ; PMLR 80:2250-2258

A Unified Framework for Structured Low-rank Matrix Learning

Pratik Jawanpuria, Bamdev Mishra ; PMLR 80:2259-2268

Efficient end-to-end learning for quantizable representations

Yeonwoo Jeong, Hyun Oh Song ; PMLR 80:2269-2278

Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks

Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken ; PMLR 80:2279-2288

Feedback-Based Tree Search for Reinforcement Learning

Daniel Jiang, Emmanuel Ekwedike, Han Liu ; PMLR 80:2289-2298

Quickshift++: Provably Good Initializations for Sample-Based Mean Shift

Heinrich Jiang, Jennifer Jang, Samory Kpotufe ; PMLR 80:2299-2308

MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels

Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, Li Fei-Fei ; PMLR 80:2309-2318

The Weighted Kendall and High-order Kernels for Permutations

Yunlong Jiao, Jean-Philippe Vert ; PMLR 80:2319-2327

Junction Tree Variational Autoencoder for Molecular Graph Generation

Wengong Jin, Regina Barzilay, Tommi Jaakkola ; PMLR 80:2328-2337

Network Global Testing by Counting Graphlets

Jiashun Jin, Zheng Tracy Ke, Shengming Luo ; PMLR 80:2338-2346

Regret Minimization for Partially Observable Deep Reinforcement Learning

Peter Jin, Kurt Keutzer, Sergey Levine ; PMLR 80:2347-2356

WSNet: Compact and Efficient Networks Through Weight Sampling

Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic, Jiashi Feng, Shuicheng Yan ; PMLR 80:2357-2366

Large-Scale Cox Process Inference using Variational Fourier Features

ST John, James Hensman ; PMLR 80:2367-2375

Composite Functional Gradient Learning of Generative Adversarial Models

Rie Johnson, Tong Zhang ; PMLR 80:2376-2384

Kronecker Recurrent Units

Cijo Jose, Moustapha Cisse, Francois Fleuret ; PMLR 80:2385-2394

Fast Decoding in Sequence Models Using Discrete Latent Variables

Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, Noam Shazeer ; PMLR 80:2395-2404

Kernel Recursive ABC: Point Estimation with Intractable Likelihood

Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu ; PMLR 80:2405-2414

Efficient Neural Audio Synthesis

Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron Oord, Sander Dieleman, Koray Kavukcuoglu ; PMLR 80:2415-2424

Learning Diffusion using Hyperparameters

Dimitris Kalimeris, Yaron Singer, Karthik Subbian, Udi Weinsberg ; PMLR 80:2425-2433

Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit

Sreejith Kallummil, Sheetal Kalyani ; PMLR 80:2434-2443

Residual Unfairness in Fair Machine Learning from Prejudiced Data

Nathan Kallus, Angela Zhou ; PMLR 80:2444-2453

Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations

Ashwin Kalyan, Stefan Lee, Anitha Kannan, Dhruv Batra ; PMLR 80:2454-2463

Semi-Supervised Learning via Compact Latent Space Clustering

Konstantinos Kamnitsas, Daniel Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori ; PMLR 80:2464-2473

Policy Optimization with Demonstrations

Bingyi Kang, Zequn Jie, Jiashi Feng ; PMLR 80:2474-2483

Improving Sign Random Projections With Additional Information

Keegan Kang, Wong Wei Pin ; PMLR 80:2484-2492

Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games

Ehsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin, Volkan Cevher ; PMLR 80:2493-2501

Continual Reinforcement Learning with Complex Synapses

Christos Kaplanis, Murray Shanahan, Claudia Clopath ; PMLR 80:2502-2511

LaVAN: Localized and Visible Adversarial Noise

Danny Karmon, Daniel Zoran, Yoav Goldberg ; PMLR 80:2512-2520

Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis

Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra ; PMLR 80:2521-2529

Not All Samples Are Created Equal: Deep Learning with Importance Sampling

Angelos Katharopoulos, Francois Fleuret ; PMLR 80:2530-2539

Feasible Arm Identification

Julian Katz-Samuels, Clay Scott ; PMLR 80:2540-2548

Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints

Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi ; PMLR 80:2549-2558

Focused Hierarchical RNNs for Conditional Sequence Processing

Nan Rosemary Ke, Konrad Żołna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler, Yoshua Bengio, Joelle Pineau, Laurent Charlin, Christopher Pal ; PMLR 80:2559-2568

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu ; PMLR 80:2569-2577

Improved nearest neighbor search using auxiliary information and priority functions

Omid Keivani, Kaushik Sinha ; PMLR 80:2578-2586

ContextNet: Deep learning for Star Galaxy Classification

Noble Kennamer, David Kirkby, Alexander Ihler, Francisco Javier Sanchez-Lopez ; PMLR 80:2587-2595

Frank-Wolfe with Subsampling Oracle

Thomas Kerdreux, Fabian Pedregosa, Alexandre d’Aspremont ; PMLR 80:2596-2605

Convergence guarantees for a class of non-convex and non-smooth optimization problems

Koulik Khamaru, Martin Wainwright ; PMLR 80:2606-2615

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava ; PMLR 80:2616-2625

Geometry Score: A Method For Comparing Generative Adversarial Networks

Valentin Khrulkov, Ivan Oseledets ; PMLR 80:2626-2634

Blind Justice: Fairness with Encrypted Sensitive Attributes

Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller ; PMLR 80:2635-2644

Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data

Minyoung Kim ; PMLR 80:2645-2653

Disentangling by Factorising

Hyunjik Kim, Andriy Mnih ; PMLR 80:2654-2663

Self-Bounded Prediction Suffix Tree via Approximate String Matching

Dongwoo Kim, Christian Walder ; PMLR 80:2664-2672

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory sayres ; PMLR 80:2673-2682

Semi-Amortized Variational Autoencoders

Yoon Kim, Sam Wiseman, Andrew Miller, David Sontag, Alexander Rush ; PMLR 80:2683-2692

Neural Relational Inference for Interacting Systems

Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel ; PMLR 80:2693-2702

An Alternative View: When Does SGD Escape Local Minima?

Robert Kleinberg, Yuanzhi Li, Yang Yuan ; PMLR 80:2703-2712

Crowdsourcing with Arbitrary Adversaries

Matthaeus Kleindessner, Pranjal Awasthi ; PMLR 80:2713-2722

Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection

Jeremias Knoblauch, Theodoros Damoulas ; PMLR 80:2723-2732

Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework

Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamas Keviczky ; PMLR 80:2733-2741

Nonconvex Optimization for Regression with Fairness Constraints

Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao ; PMLR 80:2742-2751

On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups

Risi Kondor, Shubhendu Trivedi ; PMLR 80:2752-2760

Compiling Combinatorial Prediction Games

Frederic Koriche ; PMLR 80:2761-2770

Dynamic Evaluation of Neural Sequence Models

Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals ; PMLR 80:2771-2780

Semiparametric Contextual Bandits

Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis ; PMLR 80:2781-2790

Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice

Alan Kuhnle, J. David Smith, Victoria G.Crawford, My T. Thai ; PMLR 80:2791-2800

Accurate Uncertainties for Deep Learning Using Calibrated Regression

Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon ; PMLR 80:2801-2809

Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings

Aviral Kumar, Sunita Sarawagi, Ujjwal Jain ; PMLR 80:2810-2819

Data-Dependent Stability of Stochastic Gradient Descent

Ilja Kuzborskij, Christoph Lampert ; PMLR 80:2820-2829

Explicit Inductive Bias for Transfer Learning with Convolutional Networks

Xuhong LI, Yves Grandvalet, Franck Davoine ; PMLR 80:2830-2839

Understanding the Loss Surface of Neural Networks for Binary Classification

Shiyu Liang, Ruoyu Sun, Yixuan Li, Rayadurgam Srikant ; PMLR 80:2840-2849

Mixed batches and symmetric discriminators for GAN training

Thomas Lucas, Corentin Tallec, Yann Ollivier, Jakob Verbeek ; PMLR 80:2850-2859

Binary Partitions with Approximate Minimum Impurity

Eduardo S. Laber, Marco Molinaro, Felipe A. Mello Pereira ; PMLR 80:2860-2868

Canonical Tensor Decomposition for Knowledge Base Completion

Timothee Lacroix, Nicolas Usunier, Guillaume Obozinski ; PMLR 80:2869-2878

Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks

Brenden Lake, Marco Baroni ; PMLR 80:2879-2888

An Estimation and Analysis Framework for the Rasch Model

Andrew Lan, Mung Chiang, Christoph Studer ; PMLR 80:2889-2897

Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering

Jan-Hendrik Lange, Andreas Karrenbauer, Bjoern Andres ; PMLR 80:2898-2907

Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global

Thomas Laurent, James Brecht ; PMLR 80:2908-2913

The Multilinear Structure of ReLU Networks

Thomas Laurent, James Brecht ; PMLR 80:2914-2922

Hierarchical Imitation and Reinforcement Learning

Hoang Le, Nan Jiang, Alekh Agarwal, Miroslav Dudik, Yisong Yue, Hal Daumé ; PMLR 80:2923-2932

Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace

Yoonho Lee, Seungjin Choi ; PMLR 80:2933-2942

Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling

Kyowoon Lee, Sol-A Kim, Jaesik Choi, Seong-Whan Lee ; PMLR 80:2943-2952

Gated Path Planning Networks

Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov ; PMLR 80:2953-2961

Deep Asymmetric Multi-task Feature Learning

Hae Beom Lee, Eunho Yang, Sung Ju Hwang ; PMLR 80:2962-2970

Noise2Noise: Learning Image Restoration without Clean Data

Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila ; PMLR 80:2971-2980

Out-of-sample extension of graph adjacency spectral embedding

Keith Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe ; PMLR 80:2981-2990

An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks

Qianxiao Li, Shuji Hao ; PMLR 80:2991-3000

Towards Binary-Valued Gates for Robust LSTM Training

Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, Tie-Yan Liu ; PMLR 80:3001-3010

On the Limitations of First-Order Approximation in GAN Dynamics

Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt ; PMLR 80:3011-3019

Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering

Pan Li, Olgica Milenkovic ; PMLR 80:3020-3029

The Well-Tempered Lasso

Yuanzhi Li, Yoram Singer ; PMLR 80:3030-3038

Estimation of Markov Chain via Rank-constrained Likelihood

Xudong Li, Mengdi Wang, Anru Zhang ; PMLR 80:3039-3048

Asynchronous Decentralized Parallel Stochastic Gradient Descent

Xiangru Lian, Wei Zhang, Ce Zhang, Ji Liu ; PMLR 80:3049-3058

RLlib: Abstractions for Distributed Reinforcement Learning

Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica ; PMLR 80:3059-3068

On the Spectrum of Random Features Maps of High Dimensional Data

Zhenyu Liao, Romain Couillet ; PMLR 80:3069-3077

The Dynamics of Learning: A Random Matrix Approach

Zhenyu Liao, Romain Couillet ; PMLR 80:3078-3087

Reviving and Improving Recurrent Back-Propagation

Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel ; PMLR 80:3088-3097

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Junhong Lin, Volkan Cevher ; PMLR 80:3098-3107

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Junhong Lin, Volkan Cevher ; PMLR 80:3108-3117

Level-Set Methods for Finite-Sum Constrained Convex Optimization

Qihang Lin, Runchao Ma, Tianbao Yang ; PMLR 80:3118-3127

Detecting and Correcting for Label Shift with Black Box Predictors

Zachary Lipton, Yu-Xiang Wang, Alexander Smola ; PMLR 80:3128-3136

Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression

Haitao Liu, Jianfei Cai, Yi Wang, Yew Soon Ong ; PMLR 80:3137-3146

Towards Black-box Iterative Machine Teaching

Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James Rehg, Le Song ; PMLR 80:3147-3155

Delayed Impact of Fair Machine Learning

Lydia Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt ; PMLR 80:3156-3164

A Two-Step Computation of the Exact GAN Wasserstein Distance

Huidong Liu, Xianfeng GU, Dimitris Samaras ; PMLR 80:3165-3174

Open Category Detection with PAC Guarantees

Si Liu, Risheek Garrepalli, Thomas Dietterich, Alan Fern, Dan Hendrycks ; PMLR 80:3175-3184

Fast Variance Reduction Method with Stochastic Batch Size

Xuanqing Liu, Cho-Jui Hsieh ; PMLR 80:3185-3194

Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate

Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang ; PMLR 80:3195-3203

On Matching Pursuit and Coordinate Descent

Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Raetsch, Bernhard Schölkopf, Sebastian Stich, Martin Jaggi ; PMLR 80:3204-3213

PDE-Net: Learning PDEs from Data

Zichao Long, Yiping Lu, Xianzhong Ma, Bin Dong ; PMLR 80:3214-3222

Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap

Miles Lopes, Shusen Wang, Michael Mahoney ; PMLR 80:3223-3232

Constraining the Dynamics of Deep Probabilistic Models

Marco Lorenzi, Maurizio Filippone ; PMLR 80:3233-3242

Spectrally Approximating Large Graphs with Smaller Graphs

Andreas Loukas, Pierre Vandergheynst ; PMLR 80:3243-3252

The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference

Hao Lu, Yuan Cao, Junwei Lu, Han Liu, Zhaoran Wang ; PMLR 80:3253-3262

Accelerating Greedy Coordinate Descent Methods

Haihao Lu, Robert Freund, Vahab Mirrokni ; PMLR 80:3263-3272

Structured Variationally Auto-encoded Optimization

Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai, Neil D. Lawrence ; PMLR 80:3273-3281

Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations

Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong ; PMLR 80:3282-3291

End-to-end Active Object Tracking via Reinforcement Learning

Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang ; PMLR 80:3292-3301

Competitive Caching with Machine Learned Advice

Thodoris Lykouris, Sergei Vassilvtiskii ; PMLR 80:3302-3311

Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design

Wenlong Lyu, Fan Yang, Changhao Yan, Dian Zhou, Xuan Zeng ; PMLR 80:3312-3320

Celer: a Fast Solver for the Lasso with Dual Extrapolation

Mathurin Massias, Joseph Salmon, Alexandre Gramfort ; PMLR 80:3321-3330

The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning

Siyuan Ma, Raef Bassily, Mikhail Belkin ; PMLR 80:3331-3340

Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers

Yao Ma, Alexander Olshevsky, Csaba Szepesvari, Venkatesh Saligrama ; PMLR 80:3341-3350

Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion

Cong Ma, Kaizheng Wang, Yuejie Chi, Yuxin Chen ; PMLR 80:3351-3360

Dimensionality-Driven Learning with Noisy Labels

Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah M. Erfani, Shu-Tao Xia, Sudanthi Wijewickrema, James Bailey ; PMLR 80:3361-3370

Approximate message passing for amplitude based optimization

Junjie Ma, Ji Xu, Arian Maleki ; PMLR 80:3371-3380

Learning Adversarially Fair and Transferable Representations

David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel ; PMLR 80:3381-3390

An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning

Dhruv Malik, Malayandi Palaniappan, Jaime Fisac, Dylan Hadfield-Menell, Stuart Russell, Anca Dragan ; PMLR 80:3391-3399

Iterative Amortized Inference

Joseph Marino, Yisong Yue, Stephan Mandt ; PMLR 80:3400-3409

Streaming Principal Component Analysis in Noisy Settings

Teodor Vanislavov Marinov, Poorya Mianjy, Raman Arora ; PMLR 80:3410-3419

Fast Approximate Spectral Clustering for Dynamic Networks

Lionel Martin, Andreas Loukas, Pierre Vandergheynst ; PMLR 80:3420-3429

Bayesian Model Selection for Change Point Detection and Clustering

Othmane Mazhar, Cristian Rojas, Carlo Fischione, Mohammad Reza Hesamzadeh ; PMLR 80:3430-3439

Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization

Mark McLeod, Stephen Roberts, Michael A. Osborne ; PMLR 80:3440-3449

Bounds on the Approximation Power of Feedforward Neural Networks

Mohammad Mehrabi, Aslan Tchamkerten, MANSOOR YOUSEFI ; PMLR 80:3450-3458

Differentiable Dynamic Programming for Structured Prediction and Attention

Arthur Mensch, Mathieu Blondel ; PMLR 80:3459-3468

Ranking Distributions based on Noisy Sorting

Adil El Mesaoudi-Paul, Eyke Hüllermeier, Robert Busa-Fekete ; PMLR 80:3469-3477

Which Training Methods for GANs do actually Converge?

Lars Mescheder, Andreas Geiger, Sebastian Nowozin ; PMLR 80:3478-3487

Configurable Markov Decision Processes

Alberto Maria Metelli, Mirco Mutti, Marcello Restelli ; PMLR 80:3488-3497

prDeep: Robust Phase Retrieval with a Flexible Deep Network

Christopher Metzler, Phillip Schniter, Ashok Veeraraghavan, baraniuk ; PMLR 80:3498-3507

Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing-and Back

Elliot Meyerson, Risto Miikkulainen ; PMLR 80:3508-3517

The Hidden Vulnerability of Distributed Learning in Byzantium

El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault ; PMLR 80:3518-3527

Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization

Poorya Mianjy, Raman Arora ; PMLR 80:3528-3536

On the Implicit Bias of Dropout

Poorya Mianjy, Raman Arora, Rene Vidal ; PMLR 80:3537-3545

One-Shot Segmentation in Clutter

Claudio Michaelis, Matthias Bethge, Alexander Ecker ; PMLR 80:3546-3555

Differentiable plasticity: training plastic neural networks with backpropagation

Thomas Miconi, Kenneth Stanley, Jeff Clune ; PMLR 80:3556-3565

Training Neural Machines with Trace-Based Supervision

Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevic, Timon Gehr, Martin Vechev ; PMLR 80:3566-3574

Differentiable Abstract Interpretation for Provably Robust Neural Networks

Matthew Mirman, Timon Gehr, Martin Vechev ; PMLR 80:3575-3583

A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning

Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini ; PMLR 80:3584-3592

Data Summarization at Scale: A Two-Stage Submodular Approach

Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi ; PMLR 80:3593-3602

The Hierarchical Adaptive Forgetting Variational Filter

Vincent Moens ; PMLR 80:3603-3612

Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings

Aryan Mokhtari, Hamed Hassani, Amin Karbasi ; PMLR 80:3613-3622

DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding

Thomas Moreau, Laurent Oudre, Nicolas Vayatis ; PMLR 80:3623-3631

WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models.

Marine Le Morvan, Jean-Philippe Vert ; PMLR 80:3632-3641

Dropout Training, Data-dependent Regularization, and Generalization Bounds

Wenlong Mou, Yuchen Zhou, Jun Gao, Liwei Wang ; PMLR 80:3642-3650

Kernelized Synaptic Weight Matrices

Lorenz Muller, Julien Martel, Giacomo Indiveri ; PMLR 80:3651-3660

Rapid Adaptation with Conditionally Shifted Neurons

Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri, Adam Trischler ; PMLR 80:3661-3670

On the Relationship between Data Efficiency and Error for Uncertainty Sampling

Stephen Mussmann, Percy Liang ; PMLR 80:3671-3679

Fitting New Speakers Based on a Short Untranscribed Sample

Eliya Nachmani, Adam Polyak, Yaniv Taigman, Lior Wolf ; PMLR 80:3680-3688

Smoothed Action Value Functions for Learning Gaussian Policies

Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans ; PMLR 80:3689-3697

Nearly Optimal Robust Subspace Tracking

Praneeth Narayanamurthy, Namrata Vaswani ; PMLR 80:3698-3706

Stochastic Proximal Algorithms for AUC Maximization

Michael Natole, Yiming Ying, Siwei Lyu ; PMLR 80:3707-3716

Mitigating Bias in Adaptive Data Gathering via Differential Privacy

Seth Neel, Aaron Roth ; PMLR 80:3717-3726

Optimization Landscape and Expressivity of Deep CNNs

Quynh Nguyen, Matthias Hein ; PMLR 80:3727-3736

Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions

Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein ; PMLR 80:3737-3746

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

Lam Nguyen, Phuong Ha Nguyen, Marten Dijk, Peter Richtarik, Katya Scheinberg, Martin Takac ; PMLR 80:3747-3755

Active Testing: An Efficient and Robust Framework for Estimating Accuracy

Phuc Nguyen, Deva Ramanan, Charless Fowlkes ; PMLR 80:3756-3765

On Learning Sparsely Used Dictionaries from Incomplete Samples

Thanh Nguyen, Akshay Soni, Chinmay Hegde ; PMLR 80:3766-3775

Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry

Maximillian Nickel, Douwe Kiela ; PMLR 80:3776-3785

State Space Gaussian Processes with Non-Gaussian Likelihood

Hannes Nickisch, Arno Solin, Alexander Grigorevskiy ; PMLR 80:3786-3795

SparseMAP: Differentiable Sparse Structured Inference

Vlad Niculae, Andre Martins, Mathieu Blondel, Claire Cardie ; PMLR 80:3796-3805

A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations

Weili Nie, Yang Zhang, Ankit Patel ; PMLR 80:3806-3815

Functional Gradient Boosting based on Residual Network Perception

Atsushi Nitanda, Taiji Suzuki ; PMLR 80:3816-3825

Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams

Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic, Amir Zandieh, Aidasadat Mousavifar, Ola Svensson ; PMLR 80:3826-3835

The Uncertainty Bellman Equation and Exploration

Brendan O’Donoghue, Ian Osband, Remi Munos, Volodymyr Mnih ; PMLR 80:3836-3845

Is Generator Conditioning Causally Related to GAN Performance?

Augustus Odena, Jacob Buckman, Catherine Olsson, Tom Brown, Christopher Olah, Colin Raffel, Ian Goodfellow ; PMLR 80:3846-3855

Learning in Reproducing Kernel Krein Spaces

Dino Oglic, Thomas Gaertner ; PMLR 80:3856-3864

BOCK : Bayesian Optimization with Cylindrical Kernels

ChangYong Oh, Efstratios Gavves, Max Welling ; PMLR 80:3865-3874

Self-Imitation Learning

Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee ; PMLR 80:3875-3884

A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks

Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira ; PMLR 80:3885-3894

Transformation Autoregressive Networks

Junier Oliva, Avinava Dubey, Manzil Zaheer, Barnabas Poczos, Ruslan Salakhutdinov, Eric Xing, Jeff Schneider ; PMLR 80:3895-3904

Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches

Simon Olofsson, Marc Deisenroth, Ruth Misener ; PMLR 80:3905-3914

Parallel WaveNet: Fast High-Fidelity Speech Synthesis

Aaron Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George Driessche, Edward Lockhart, Luis Cobo, Florian Stimberg, Norman Casagrande, Dominik Grewe, Seb Noury, Sander Dieleman, Erich Elsen, Nal Kalchbrenner, Heiga Zen, Alex Graves, Helen King, Tom Walters, Dan Belov, Demis Hassabis ; PMLR 80:3915-3923

Learning Localized Spatio-Temporal Models From Streaming Data

Muhammad Osama, Dave Zachariah, Thomas Schön ; PMLR 80:3924-3932

Autoregressive Quantile Networks for Generative Modeling

Georg Ostrovski, Will Dabney, Remi Munos ; PMLR 80:3933-3942

Efficient First-Order Algorithms for Adaptive Signal Denoising

Dmitrii Ostrovskii, Zaid Harchaoui ; PMLR 80:3943-3952

Analyzing Uncertainty in Neural Machine Translation

Myle Ott, Michael Auli, David Grangier, Marc’Aurelio Ranzato ; PMLR 80:3953-3962

Learning Compact Neural Networks with Regularization

Samet Oymak ; PMLR 80:3963-3972

Tree Edit Distance Learning via Adaptive Symbol Embeddings

Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Barbara Hammer ; PMLR 80:3973-3982

Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control

Yangchen Pan, Amir-massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover, Daniel Nikovski ; PMLR 80:3983-3992

Learning to Speed Up Structured Output Prediction

Xingyuan Pan, Vivek Srikumar ; PMLR 80:3993-4002

Theoretical Analysis of Image-to-Image Translation with Adversarial Learning

Xudong Pan, Mi Zhang, Daizong Ding ; PMLR 80:4003-4012

Max-Mahalanobis Linear Discriminant Analysis Networks

Tianyu Pang, Chao Du, Jun Zhu ; PMLR 80:4013-4022

Stochastic Variance-Reduced Policy Gradient

Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli ; PMLR 80:4023-4032

Learning Independent Causal Mechanisms

Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf ; PMLR 80:4033-4041

Time Limits in Reinforcement Learning

Fabio Pardo, Arash Tavakoli, Vitaly Levdik, Petar Kormushev ; PMLR 80:4042-4051

Image Transformer

Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran ; PMLR 80:4052-4061

PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya ; PMLR 80:4062-4071

High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach

Tim Pearce, Alexandra Brintrup, Mohamed Zaki, Andy Neely ; PMLR 80:4072-4081

Adaptive Three Operator Splitting

Fabian Pedregosa, Gauthier Gidel ; PMLR 80:4082-4091

Efficient Neural Architecture Search via Parameter Sharing

Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, Jeff Dean ; PMLR 80:4092-4101

Bandits with Delayed, Aggregated Anonymous Feedback

Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvari, Steffen Grunewalder ; PMLR 80:4102-4110

Constant-Time Predictive Distributions for Gaussian Processes

Geoff Pleiss, Jacob Gardner, Kilian Weinberger, Andrew Gordon Wilson ; PMLR 80:4111-4120

Local Convergence Properties of SAGA/Prox-SVRG and Acceleration

Clarice Poon, Jingwei Liang, Carola-Bibiane Schoenlieb ; PMLR 80:4121-4129

Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory

Guillaume Pouliot ; PMLR 80:4130-4137

Learning Dynamics of Linear Denoising Autoencoders

Arnu Pretorius, Steve Kroon, Herman Kamper ; PMLR 80:4138-4147

JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

Yunchen Pu, Shuyang Dai, Zhe Gan, Weiyao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin ; PMLR 80:4148-4157

Selecting Representative Examples for Program Synthesis

Yewen Pu, Zachery Miranda, Armando Solar-Lezama, Leslie Kaelbling ; PMLR 80:4158-4167

Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction

Siyuan Qi, Baoxiong Jia, Song-Chun Zhu ; PMLR 80:4168-4176

Do Outliers Ruin Collaboration?

Mingda Qiao ; PMLR 80:4177-4184

Gradually Updated Neural Networks for Large-Scale Image Recognition

Siyuan Qiao, Zhishuai Zhang, Wei Shen, Bo Wang, Alan Yuille ; PMLR 80:4185-4194

DCFNet: Deep Neural Network with Decomposed Convolutional Filters

Qiang Qiu, Xiuyuan Cheng, Robert Calderbank, Guillermo Sapiro ; PMLR 80:4195-4204

Non-convex Conditional Gradient Sliding

Chao Qu, Yan Li, Huan Xu ; PMLR 80:4205-4214

Machine Theory of Mind

Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick ; PMLR 80:4215-4224

Fast Parametric Learning with Activation Memorization

Jack Rae, Chris Dyer, Peter Dayan, Timothy Lillicrap ; PMLR 80:4225-4234

Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?

Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc Le, Jon Kleinberg ; PMLR 80:4235-4243

Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation

Hugo Raguet, Loic Landrieu ; PMLR 80:4244-4253

Modeling Others using Oneself in Multi-Agent Reinforcement Learning

Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus ; PMLR 80:4254-4263

On Nesting Monte Carlo Estimators

Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington ; PMLR 80:4264-4273

Tighter Variational Bounds are Not Necessarily Better

Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh ; PMLR 80:4274-4282

SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate

Aaditya Ramdas, Tijana Zrnic, Martin Wainwright, Michael Jordan ; PMLR 80:4283-4291

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid, Mikayel Samvelyan, Christian Schroeder Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson ; PMLR 80:4292-4301

Gradient Coding from Cyclic MDS Codes and Expander Graphs

Netanel Raviv, Rashish Tandon, Alex Dimakis, Itzhak Tamo ; PMLR 80:4302-4310

Learning Implicit Generative Models with the Method of Learned Moments

Suman Ravuri, Shakir Mohamed, Mihaela Rosca, Oriol Vinyals ; PMLR 80:4311-4320

Weightless: Lossy weight encoding for deep neural network compression

Brandon Reagan, Udit Gupta, Bob Adolf, Michael Mitzenmacher, Alexander Rush, Gu-Yeon Wei, David Brooks ; PMLR 80:4321-4330

Learning to Reweight Examples for Robust Deep Learning

Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun ; PMLR 80:4331-4340

Learning by Playing Solving Sparse Reward Tasks from Scratch

Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg ; PMLR 80:4341-4350

Been There, Done That: Meta-Learning with Episodic Recall

Samuel Ritter, Jane Wang, Zeb Kurth-Nelson, Siddhant Jayakumar, Charles Blundell, Razvan Pascanu, Matthew Botvinick ; PMLR 80:4351-4360

A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music

Adam Roberts, Jesse Engel, Colin Raffel, Curtis Hawthorne, Douglas Eck ; PMLR 80:4361-4370

Learning to Optimize Combinatorial Functions

Nir Rosenfeld, Eric Balkanski, Amir Globerson, Yaron Singer ; PMLR 80:4371-4380

Fast Information-theoretic Bayesian Optimisation

Binxin Ru, Mark McLeod, Diego Granziol, Michael A. Osborne ; PMLR 80:4381-4389

Deep One-Class Classification

Lukas Ruff, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert Vandermeulen, Alexander Binder, Emmanuel Müller, Marius Kloft ; PMLR 80:4390-4399

Augment and Reduce: Stochastic Inference for Large Categorical Distributions

Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei ; PMLR 80:4400-4409

Probabilistic Boolean Tensor Decomposition

Tammo Rukat, Chris Holmes, Christopher Yau ; PMLR 80:4410-4419

Black-Box Variational Inference for Stochastic Differential Equations

Thomas Ryder, Andrew Golightly, A. Stephen McGough, Dennis Prangle ; PMLR 80:4420-4429

Spurious Local Minima are Common in Two-Layer ReLU Neural Networks

Itay Safran, Ohad Shamir ; PMLR 80:4430-4438

Learning Equations for Extrapolation and Control

Subham Sahoo, Christoph Lampert, Georg Martius ; PMLR 80:4439-4447

Tempered Adversarial Networks

Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf ; PMLR 80:4448-4456

Representation Tradeoffs for Hyperbolic Embeddings

Frederic Sala, Chris De Sa, Albert Gu, Christopher Re ; PMLR 80:4457-4466

Graph Networks as Learnable Physics Engines for Inference and Control

Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia ; PMLR 80:4467-4476

Measuring abstract reasoning in neural networks

Adam Santoro, Felix Hill, David Barrett, Ari Morcos, Timothy Lillicrap ; PMLR 80:4477-4486

A Classification-Based Study of Covariate Shift in GAN Distributions

Shibani Santurkar, Ludwig Schmidt, Aleksander Madry ; PMLR 80:4487-4496

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

Amartya Sanyal, Matt Kusner, Adria Gascon, Varun Kanade ; PMLR 80:4497-4506

Tight Regret Bounds for Bayesian Optimization in One Dimension

Jonathan Scarlett ; PMLR 80:4507-4515

Learning with Abandonment

Sven Schmit, Ramesh Johari ; PMLR 80:4516-4524

Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care

Patrick Schwab, Emanuela Keller, Carl Muroi, David J. Mack, Christian Strässle, Walter Karlen ; PMLR 80:4525-4534

Progress & Compress: A scalable framework for continual learning

Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell ; PMLR 80:4535-4544

Multi-Fidelity Black-Box Optimization with Hierarchical Partitions

Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai ; PMLR 80:4545-4554

Overcoming Catastrophic Forgetting with Hard Attention to the Task

Joan Serra, Didac Suris, Marius Miron, Alexandros Karatzoglou ; PMLR 80:4555-4564

Bounding and Counting Linear Regions of Deep Neural Networks

Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam ; PMLR 80:4565-4573

First Order Generative Adversarial Networks

Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter ; PMLR 80:4574-4583

Finding Influential Training Samples for Gradient Boosted Decision Trees

Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten Rijke ; PMLR 80:4584-4592

Solving Partial Assignment Problems using Random Clique Complexes

Charu Sharma, Deepak Nathani, Manohar Kaul ; PMLR 80:4593-4602

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Noam Shazeer, Mitchell Stern ; PMLR 80:4603-4611

Locally Private Hypothesis Testing

Or Sheffet ; PMLR 80:4612-4621

Learning in Integer Latent Variable Models with Nested Automatic Differentiation

Daniel Sheldon, Kevin Winner, Debora Sujono ; PMLR 80:4622-4630

Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication

Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian ; PMLR 80:4631-4640

An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method

Li Shen, Peng Sun, Yitong Wang, Wei Liu, Tong Zhang ; PMLR 80:4641-4650

A Spectral Approach to Gradient Estimation for Implicit Distributions

Jiaxin Shi, Shengyang Sun, Jun Zhu ; PMLR 80:4651-4660

TACO: Learning Task Decomposition via Temporal Alignment for Control

Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, Ingmar Posner ; PMLR 80:4661-4670

CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning

Wissam Siblini, Frank Meyer, Pascale Kuntz ; PMLR 80:4671-4680

Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization

Umut Simsekli, Cagatay Yildiz, Than Huy Nguyen, A. Taylan Cemgil, Gael Richard ; PMLR 80:4681-4690

K-means clustering using random matrix sparsification

Kaushik Sinha ; PMLR 80:4691-4699

Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron

RJ Skerry-Ryan, Eric Battenberg, Ying Xiao, Yuxuan Wang, Daisy Stanton, Joel Shor, Ron Weiss, Rob Clark, Rif A. Saurous ; PMLR 80:4700-4709

An Inference-Based Policy Gradient Method for Learning Options

Matthew Smith, Herke Hoof, Joelle Pineau ; PMLR 80:4710-4719

Accelerating Natural Gradient with Higher-Order Invariance

Yang Song, Jiaming Song, Stefano Ermon ; PMLR 80:4720-4729

Knowledge Transfer with Jacobian Matching

Suraj Srinivas, Francois Fleuret ; PMLR 80:4730-4738

Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control

Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn ; PMLR 80:4739-4748

Structured Control Nets for Deep Reinforcement Learning

Mario Srouji, Jian Zhang, Ruslan Salakhutdinov ; PMLR 80:4749-4758

Approximation Algorithms for Cascading Prediction Models

Matthew Streeter ; PMLR 80:4759-4767

Learning Low-Dimensional Temporal Representations

Bing Su, Ying Wu ; PMLR 80:4768-4777

Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search

Masanori Suganuma, Mete Ozay, Takayuki Okatani ; PMLR 80:4778-4787

Stagewise Safe Bayesian Optimization with Gaussian Processes

Yanan Sui, Zhuang, Joel Burdick, Yisong Yue ; PMLR 80:4788-4796

Neural Program Synthesis from Diverse Demonstration Videos

’Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram, Joseph Lim ; PMLR 80:4797-4806

Scalable Approximate Bayesian Inference for Particle Tracking Data

Ruoxi Sun, Liam Paninski ; PMLR 80:4807-4816

Graphical Nonconvex Optimization via an Adaptive Convex Relaxation

Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang ; PMLR 80:4817-4824

Convolutional Imputation of Matrix Networks

Qingyun Sun, Mengyuan Yan, David Donoho, boyd ; PMLR 80:4825-4834

Differentiable Compositional Kernel Learning for Gaussian Processes

Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger Grosse ; PMLR 80:4835-4844

Learning the Reward Function for a Misspecified Model

Erik Talvitie ; PMLR 80:4845-4854

$D^2$: Decentralized Training over Decentralized Data

Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu ; PMLR 80:4855-4863

Neural Inverse Rendering for General Reflectance Photometric Stereo

Tatsunori Taniai, Takanori Maehara ; PMLR 80:4864-4873

Black Box FDR

Wesley Tansey, Yixin Wang, David Blei, Raul Rabadan ; PMLR 80:4874-4883

Best Arm Identification in Linear Bandits with Linear Dimension Dependency

Chao Tao, Saúl Blanco, Yuan Zhou ; PMLR 80:4884-4893

Chi-square Generative Adversarial Network

Chenyang Tao, Liqun Chen, Ricardo Henao, Jianfeng Feng, Lawrence Carin ; PMLR 80:4894-4903

Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees

Adrien Taylor, Bryan Van Scoy, Laurent Lessard ; PMLR 80:4904-4913

Bayesian Uncertainty Estimation for Batch Normalized Deep Networks

Mattias Teye, Hossein Azizpour, Kevin Smith ; PMLR 80:4914-4923

Decoupling Gradient-Like Learning Rules from Representations

Philip Thomas, Christoph Dann, Emma Brunskill ; PMLR 80:4924-4932

CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions

Kevin Tian, Teng Zhang, James Zou ; PMLR 80:4933-4942

Importance Weighted Transfer of Samples in Reinforcement Learning

Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli ; PMLR 80:4943-4952

Adversarial Regression with Multiple Learners

Liang Tong, Sixie Yu, Scott Alfeld, vorobeychik ; PMLR 80:4953-4961

Convergent TREE BACKUP and RETRACE with Function Approximation

Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent ; PMLR 80:4962-4971

Learning Longer-term Dependencies in RNNs with Auxiliary Losses

Trieu Trinh, Andrew Dai, Thang Luong, Quoc Le ; PMLR 80:4972-4981

Theoretical Analysis of Sparse Subspace Clustering with Missing Entries

Manolis Tsakiris, Rene Vidal ; PMLR 80:4982-4991

StrassenNets: Deep Learning with a Multiplication Budget

Michael Tschannen, Aran Khanna, Animashree Anandkumar ; PMLR 80:4992-5001

Invariance of Weight Distributions in Rectified MLPs

Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher ; PMLR 80:5002-5011

Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator

Stephen Tu, Benjamin Recht ; PMLR 80:5012-5021

The Mirage of Action-Dependent Baselines in Reinforcement Learning

George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard Turner, Zoubin Ghahramani, Sergey Levine ; PMLR 80:5022-5031

Adversarial Risk and the Dangers of Evaluating Against Weak Attacks

Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli, Aaron Oord ; PMLR 80:5032-5041

DVAE++: Discrete Variational Autoencoders with Overlapping Transformations

Arash Vahdat, William Macready, Zhengbing Bian, Amir Khoshaman, Evgeny Andriyash ; PMLR 80:5042-5051

Programmatically Interpretable Reinforcement Learning

Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri ; PMLR 80:5052-5061

A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization

Robin Vogel, Aurélien Bellet, Stéphan Clémençon ; PMLR 80:5062-5071

Transfer Learning via Learning to Transfer

Ying Wei, Yu Zhang, Junzhou Huang, Qiang Yang ; PMLR 80:5072-5081

Semi-Supervised Learning on Data Streams via Temporal Label Propagation

Tal Wagner, Sudipto Guha, Shiva Prasad Kasiviswanathan, Nina Mishra ; PMLR 80:5082-5091

Neural Dynamic Programming for Musical Self Similarity

Christian Walder, Dongwoo Kim ; PMLR 80:5092-5100

Thompson Sampling for Combinatorial Semi-Bandits

Siwei Wang, Wei Chen ; PMLR 80:5101-5109

PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning

Yunbo Wang, Zhifeng Gao, Mingsheng Long, Jianmin Wang, Philip S Yu ; PMLR 80:5110-5119

Analyzing the Robustness of Nearest Neighbors to Adversarial Examples

Yizhen Wang, Somesh Jha, Kamalika Chaudhuri ; PMLR 80:5120-5129

Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations

Xingyu Wang, Diego Klabjan ; PMLR 80:5130-5138

Coded Sparse Matrix Multiplication

Sinong Wang, Jiashang Liu, Ness Shroff ; PMLR 80:5139-5147

A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models

Beilun Wang, Arshdeep Sekhon, Yanjun Qi ; PMLR 80:5148-5157

Provable Variable Selection for Streaming Features

Jing Wang, Jie Shen, Ping Li ; PMLR 80:5158-5166

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ-Skerry Ryan, Eric Battenberg, Joel Shor, Ying Xiao, Ye Jia, Fei Ren, Rif A. Saurous ; PMLR 80:5167-5176

Adversarial Distillation of Bayesian Neural Network Posteriors

Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, Richard Zemel ; PMLR 80:5177-5186

Minimax Concave Penalized Multi-Armed Bandit Model with High-Dimensional Convariates

Xue Wang, Mike Mingcheng Wei, Tao Yao ; PMLR 80:5187-5195

Online Convolutional Sparse Coding with Sample-Dependent Dictionary

Yaqing Wang, Quanming Yao, James Tin-Yau Kwok, Lionel M. NI ; PMLR 80:5196-5205

Stein Variational Message Passing for Continuous Graphical Models

Dilin Wang, Zhe Zeng, Qiang Liu ; PMLR 80:5206-5214

Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions

Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni ; PMLR 80:5215-5224

Hierarchical Multi-Label Classification Networks

Jonatas Wehrmann, Ricardo Cerri, Rodrigo Barros ; PMLR 80:5225-5234

Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks

Daphna Weinshall, Gad Cohen, Dan Amir ; PMLR 80:5235-5243

Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

Gail Weiss, Yoav Goldberg, Eran Yahav ; PMLR 80:5244-5253

LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm Configuration

Gellert Weisz, Andras Gyorgy, Csaba Szepesvari ; PMLR 80:5254-5262

Deep Predictive Coding Network for Object Recognition

Haiguang Wen, Kuan Han, Junxing Shi, Yizhen Zhang, Eugenio Culurciello, Zhongming Liu ; PMLR 80:5263-5272

Towards Fast Computation of Certified Robustness for ReLU Networks

Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane Boning, Inderjit Dhillon ; PMLR 80:5273-5282

Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope

Eric Wong, Zico Kolter ; PMLR 80:5283-5292

Local Density Estimation in High Dimensions

Xian Wu, Moses Charikar, Vishnu Natchu ; PMLR 80:5293-5301

Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits

Huasen Wu, Xueying Guo, Xin Liu ; PMLR 80:5302-5310

SQL-Rank: A Listwise Approach to Collaborative Ranking

Liwei Wu, Cho-Jui Hsieh, James Sharpnack ; PMLR 80:5311-5320

Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization

Jiaxiang Wu, Weidong Huang, Junzhou Huang, Tong Zhang ; PMLR 80:5321-5329

Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training

Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha ; PMLR 80:5330-5338

Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms

Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon Du, Stuart Russell ; PMLR 80:5339-5348

Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization

Hang Wu, May Wang ; PMLR 80:5349-5358

Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions

Junru Wu, Yue Wang, Zhenyu Wu, Zhangyang Wang, Ashok Veeraraghavan, Yingyan Lin ; PMLR 80:5359-5368

Bayesian Quadrature for Multiple Related Integrals

Xiaoyue Xi, Francois-Xavier Briol, Mark Girolami ; PMLR 80:5369-5378

Model-Level Dual Learning

Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, Tie-Yan Liu ; PMLR 80:5379-5388

Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks

Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel Schoenholz, Jeffrey Pennington ; PMLR 80:5389-5398

Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis

Pengtao Xie, Wei Wu, Yichen Zhu, Eric P. Xing ; PMLR 80:5399-5408

Nonoverlap-Promoting Variable Selection

Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric P. Xing ; PMLR 80:5409-5418

Learning Semantic Representations for Unsupervised Domain Adaptation

Shaoan Xie, Zibin Zheng, Liang Chen, Chuan Chen ; PMLR 80:5419-5428

Rates of Convergence of Spectral Methods for Graphon Estimation

Jiaming Xu ; PMLR 80:5429-5438

Learning Registered Point Processes from Idiosyncratic Observations

Hongteng Xu, Lawrence Carin, Hongyuan Zha ; PMLR 80:5439-5448

Representation Learning on Graphs with Jumping Knowledge Networks

Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka ; PMLR 80:5449-5458

Learning to Explore via Meta-Policy Gradient

Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng ; PMLR 80:5459-5468

Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information

Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski ; PMLR 80:5469-5478

Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data

Ganggang Xu, Zuofeng Shang, Guang Cheng ; PMLR 80:5479-5487

Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions

Pan Xu, Tianhao Wang, Quanquan Gu ; PMLR 80:5488-5497

A Semantic Loss Function for Deep Learning with Symbolic Knowledge

Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, Guy Broeck ; PMLR 80:5498-5507

Causal Bandits with Propagating Inference

Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi ; PMLR 80:5508-5516

Active Learning with Logged Data

Songbai Yan, Kamalika Chaudhuri, Tara Javidi ; PMLR 80:5517-5526

Binary Classification with Karmic, Threshold-Quasi-Concave Metrics

Bowei Yan, Oluwasanmi Koyejo, Kai Zhong, Pradeep Ravikumar ; PMLR 80:5527-5536

Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions

Karren Yang, Abigail Katoff, Caroline Uhler ; PMLR 80:5537-5546

Dependent Relational Gamma Process Models for Longitudinal Networks

Sikun Yang, Heinz Koeppl ; PMLR 80:5547-5556

Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy

Jiasen Yang, Qiang Liu, Vinayak Rao, Jennifer Neville ; PMLR 80:5557-5566

Mean Field Multi-Agent Reinforcement Learning

Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, Jun Wang ; PMLR 80:5567-5576

Yes, but Did It Work?: Evaluating Variational Inference

Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman ; PMLR 80:5577-5586

Hierarchical Text Generation and Planning for Strategic Dialogue

Denis Yarats, Mike Lewis ; PMLR 80:5587-5595

Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under $\ell_p$ Distances

Grigory Yaroslavtsev, Adithya Vadapalli ; PMLR 80:5596-5605

Communication-Computation Efficient Gradient Coding

Min Ye, Emmanuel Abbe ; PMLR 80:5606-5615

Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach

Mao Ye, Yan Sun ; PMLR 80:5616-5625

Loss Decomposition for Fast Learning in Large Output Spaces

Ian En-Hsu Yen, Satyen Kale, Felix Yu, Daniel Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar ; PMLR 80:5626-5635

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Dong Yin, Yudong Chen, Kannan Ramchandran, Peter Bartlett ; PMLR 80:5636-5645

Semi-Implicit Variational Inference

Mingzhang Yin, Mingyuan Zhou ; PMLR 80:5646-5655

Disentangled Sequential Autoencoder

Li Yingzhen, Stephan Mandt ; PMLR 80:5656-5665

Probably Approximately Metric-Fair Learning

Gal Yona, Guy Rothblum ; PMLR 80:5666-5674

GAIN: Missing Data Imputation using Generative Adversarial Nets

Jinsung Yoon, James Jordon, Mihaela Schaar ; PMLR 80:5675-5684

RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks

Jinsung Yoon, James Jordon, Mihaela Schaar ; PMLR 80:5685-5693

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, Jure Leskovec ; PMLR 80:5694-5703

An Efficient Semismooth Newton Based Algorithm for Convex Clustering

Yancheng Yuan, Defeng Sun, Kim-Chuan Toh ; PMLR 80:5704-5712

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Alp Yurtsever, Olivier Fercoq, Francesco Locatello, Volkan Cevher ; PMLR 80:5713-5722

Orthogonal Machine Learning: Power and Limitations

Ilias Zadik, Lester Mackey, Vasilis Syrgkanis ; PMLR 80:5723-5731

Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs

Andrea Zanette, Emma Brunskill ; PMLR 80:5732-5740

Policy Optimization as Wasserstein Gradient Flows

Ruiyi Zhang, Changyou Chen, Chunyuan Li, Lawrence Carin ; PMLR 80:5741-5750

Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow

Xiao Zhang, Simon Du, Quanquan Gu ; PMLR 80:5751-5760

Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion

Richard Zhang, Salar Fattahi, Somayeh Sojoudi ; PMLR 80:5761-5770

High Performance Zero-Memory Overhead Direct Convolutions

Jiyuan Zhang, Franz Franchetti, Tze Meng Low ; PMLR 80:5771-5780

Safe Element Screening for Submodular Function Minimization

Weizhong Zhang, Bin Hong, Lin Ma, Wei Liu, Tong Zhang ; PMLR 80:5781-5790

Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms

Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu ; PMLR 80:5791-5800

Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization

Jiong Zhang, Qi Lei, Inderjit Dhillon ; PMLR 80:5801-5809

Learning Long Term Dependencies via Fourier Recurrent Units

Jiong Zhang, Yibo Lin, Zhao Song, Inderjit Dhillon ; PMLR 80:5810-5818

Tropical Geometry of Deep Neural Networks

Liwen Zhang, Gregory Naitzat, Lek-Heng Lim ; PMLR 80:5819-5827

Deep Bayesian Nonparametric Tracking

Aonan Zhang, John Paisley ; PMLR 80:5828-5836

Composable Planning with Attributes

Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer, Arthur Szlam, Rob Fergus ; PMLR 80:5837-5846

Noisy Natural Gradient as Variational Inference

Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse ; PMLR 80:5847-5856

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery

Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu ; PMLR 80:5857-5866

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar ; PMLR 80:5867-5876

Dynamic Regret of Strongly Adaptive Methods

Lijun Zhang, Tianbao Yang, jin, Zhi-Hua Zhou ; PMLR 80:5877-5886

Inter and Intra Topic Structure Learning with Word Embeddings

He Zhao, Lan Du, Wray Buntine, Mingyuan Zhou ; PMLR 80:5887-5896

Adversarially Regularized Autoencoders

Junbo Zhao, Yoon Kim, Kelly Zhang, Alexander Rush, Yann LeCun ; PMLR 80:5897-5906

MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning

Bo Zhao, Xinwei Sun, Yanwei Fu, Yuan Yao, Yizhou Wang ; PMLR 80:5907-5916

Composite Marginal Likelihood Methods for Random Utility Models

Zhibing Zhao, Lirong Xia ; PMLR 80:5917-5926

Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data

Shuai Zheng, James Tin-Yau Kwok ; PMLR 80:5927-5935

A Robust Approach to Sequential Information Theoretic Planning

Sue Zheng, Jason Pacheco, John Fisher ; PMLR 80:5936-5944

Revealing Common Statistical Behaviors in Heterogeneous Populations

Andrey Zhitnikov, Rotem Mulayoff, Tomer Michaeli ; PMLR 80:5945-5954

Understanding Generalization and Optimization Performance of Deep CNNs

Pan Zhou, Jiashi Feng ; PMLR 80:5955-5964

Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?

Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter Glynn, Yinyu Ye, Li-Jia Li, Li Fei-Fei ; PMLR 80:5965-5974

A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates

Kaiwen Zhou, Fanhua Shang, James Cheng ; PMLR 80:5975-5984

Stochastic Variance-Reduced Cubic Regularized Newton Method

Dongruo Zhou, Pan Xu, Quanquan Gu ; PMLR 80:5985-5994

Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors

Yichi Zhou, Jun Zhu, Jingwei Zhuo ; PMLR 80:5995-6003

Distributed Nonparametric Regression under Communication Constraints

Yuancheng Zhu, John Lafferty ; PMLR 80:6004-6012

Message Passing Stein Variational Gradient Descent

Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang ; PMLR 80:6013-6022

Stochastic Variance-Reduced Hamilton Monte Carlo Methods

Difan Zou, Pan Xu, Quanquan Gu ; PMLR 80:6023-6032

Hierarchical Long-term Video Prediction without Supervision

Nevan wichers, Ruben Villegas, Dumitru Erhan, Honglak Lee ; PMLR 80:6033-6041

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