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: Simple 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, Gerhard Neumann, Mingjun Zhong ; 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

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

Davide Bacciu, Federico Errica, Alessio Micheli ; PMLR 80:294-303

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

Wenruo Bai, Jeff Bilmes ; PMLR 80:304-313

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 80:314-323

SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions

Chandrajit Bajaj, Tingran Gao, Zihang He, Qixing Huang, Zhenxiao Liang ; PMLR 80:324-333

A Boo(n) for Evaluating Architecture Performance

Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst ; PMLR 80:334-343

Learning to Branch

Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik ; PMLR 80:344-353

The Mechanics of n-Player Differentiable Games

David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel ; PMLR 80:354-363

Spline Filters For End-to-End Deep Learning

Randall Balestriero, Romain Cosentino, Herve Glotin, Richard Baraniuk ; PMLR 80:364-373

A Spline Theory of Deep Learning

Randall Balestriero, baraniuk ; PMLR 80:374-383

Approximation Guarantees for Adaptive Sampling

Eric Balkanski, Yaron Singer ; PMLR 80:384-393

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

Borja Balle, Yu-Xiang Wang ; PMLR 80:394-403

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

Lukas Balles, Philipp Hennig ; PMLR 80:404-413

Differentially Private Database Release via Kernel Mean Embeddings

Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf ; PMLR 80:414-422

Improving Optimization for Models With Continuous Symmetry Breaking

Robert Bamler, Stephan Mandt ; PMLR 80:423-432

Improved Training of Generative Adversarial Networks Using Representative Features

Duhyeon Bang, Hyunjung Shim ; PMLR 80:433-442

Using Inherent Structures to design Lean 2-layer RBMs

Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya ; PMLR 80:443-451

Classification from Pairwise Similarity and Unlabeled Data

Han Bao, Gang Niu, Masashi Sugiyama ; PMLR 80:452-461

Bayesian Optimization of Combinatorial Structures

Ricardo Baptista, Matthias Poloczek ; PMLR 80:462-471

Geodesic Convolutional Shape Optimization

Pierre Baque, Edoardo Remelli, Francois Fleuret, Pascal Fua ; PMLR 80:472-481

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 80:482-490

Testing Sparsity over Known and Unknown Bases

Siddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal ; PMLR 80:491-500

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 80:501-510

Measuring abstract reasoning in neural networks

David Barrett, Felix Hill, Adam Santoro, Ari Morcos, Timothy Lillicrap ; PMLR 80:511-520

Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks

Peter Bartlett, Dave Helmbold, Philip Long ; PMLR 80:521-530

Mutual Information Neural Estimation

Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, Devon Hjelm ; PMLR 80:531-540

To Understand Deep Learning We Need to Understand Kernel Learning

Mikhail Belkin, Siyuan Ma, Soumik Mandal ; PMLR 80:541-549

Understanding and Simplifying One-Shot Architecture Search

Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc Le ; PMLR 80:550-559

signSGD: Compressed Optimisation for Non-Convex Problems

Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar ; PMLR 80:560-569

Distributed Clustering via LSH Based Data Partitioning

Aditya Bhaskara, Maheshakya Wijewardena ; PMLR 80:570-579

Autoregressive Convolutional Neural Networks for Asynchronous Time Series

Mikolaj Binkowski, Gautier Marti, Philippe Donnat ; PMLR 80:580-589

Adaptive Sampled Softmax with Kernel Based Sampling

Guy Blanc, Steffen Rendle ; PMLR 80:590-599

Optimizing the Latent Space of Generative Networks

Piotr Bojanowski, Armand Joulin, David Lopez-Pas, Arthur Szlam ; PMLR 80:600-609

NetGAN: Generating Graphs via Random Walks

Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann ; PMLR 80:610-619

A Progressive Batching L-BFGS Method for Machine Learning

Raghu Bollapragada, Jorge Nocedal, Dheevatsa Mudigere, Hao-Jun Shi, Ping Tak Peter Tang ; PMLR 80:620-629

Prediction Rule Reshaping

Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty ; PMLR 80:630-638

QuantTree: Histograms for Change Detection in Multivariate Data Streams

Giacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Macciò ; PMLR 80:639-648

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

Vladimir Braverman, Stephen Chestnut, Robert Krauthgamer, Yi Li, David Woodruff, Lin Yang ; PMLR 80:649-658

Predict and Constrain: Modeling Cardinality in Deep Structured Prediction

Nataly Brukhim, Amir Globerson ; PMLR 80:659-667

Quasi-Monte Carlo Variational Inference

Alexander Buchholz, Florian Wenzel, Stephan Mandt ; PMLR 80:668-677

Path-Level Network Transformation for Efficient Architecture Search

Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu ; PMLR 80:678-687

Improved large-scale graph learning through ridge spectral sparsification

Daniele Calandriello, Alessandro Lazaric, Ioannis Koutis, Michal Valko ; PMLR 80:688-697

Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent

Trevor Campbell, Tamara Broderick ; PMLR 80:698-706

Adversarial Learning with Local Coordinate Coding

Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan ; PMLR 80:707-715

Fair and Diverse DPP-Based Data Summarization

Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth Vishnoi ; PMLR 80:716-725

Conditional Noise-Contrastive Estimation of Unnormalised Models

Ciwan Ceylan, Michael U. Gutmann ; PMLR 80:726-734

Adversarial Time-to-Event Modeling

Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein, Lawrence Carin Duke, Ricardo Henao ; PMLR 80:735-744

Stability and Generalization of Learning Algorithms that Converge to Global Optima

Zachary Charles, Dimitris Papailiopoulos ; PMLR 80:745-754

Learning and Memorization

Satrajit Chatterjee ; PMLR 80:755-763

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo

Niladri Chatterji, Nicolas Flammarion, Yian Ma, Peter Bartlett, Michael Jordan ; PMLR 80:764-773

Hierarchical Clustering with Structural Constraints

Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar ; PMLR 80:774-783

Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series

Zhengping Che, Sanjay Purushotham, Guangyu Li, Bo Jiang, Yan Liu ; PMLR 80:784-793

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

Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, Andrew Rabinovich ; PMLR 80:794-803

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

Lin Chen, Moran Feldman, Amin Karbasi ; PMLR 80:804-813

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

Lin Chen, Christopher Harshaw, Hamed Hassani, Amin Karbasi ; PMLR 80:814-823

Continuous-Time Flows for Efficient Inference and Density Estimation

Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin Duke ; PMLR 80:824-833

Scalable Bilinear Pi Learning Using State and Action Features

Yichen Chen, Lihong Li, Mengdi Wang ; PMLR 80:834-843

Stein Points

Wilson Ye Chen, Lester Mackey, Jackson Gorham, Francois-Xavier Briol, Chris Oates ; PMLR 80:844-853

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

Ting Chen, Martin Renqiang Min, Yizhou Sun ; PMLR 80:854-863

PixelSNAIL: An Improved Autoregressive Generative Model

XI Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel ; PMLR 80:864-872

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

Minmin Chen, Jeffrey Pennington, Samuel Schoenholz ; PMLR 80:873-882

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

Jianbo Chen, Le Song, Martin Wainwright, Michael Jordan ; PMLR 80:883-892

Variational Inference and Model Selection with Generalized Evidence Bounds

Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, Lawrence Carin Duke ; PMLR 80:893-902

DRACO: Byzantine-resilient Distributed Training via Redundant Gradients

Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos ; PMLR 80:903-912

SADAGRAD: Strongly Adaptive Stochastic Gradient Methods

Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang ; PMLR 80:913-921

Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization

Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu ; PMLR 80:922-931

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

Di Chen, Yexiang Xue, Carla Gomes ; PMLR 80:932-941

Stochastic Training of Graph Convolutional Networks with Variance Reduction

Jianfei Chen, Jun Zhu, Le Song ; PMLR 80:942-950

Extreme Learning to Rank via Low Rank Assumption

Minhao Cheng, Ian Davidson, Cho-Jui Hsieh ; PMLR 80:951-960

Learning a Mixture of Two Multinomial Logits

Flavio Chierichetti, Ravi Kumar, Andrew Tomkins ; PMLR 80:961-969

Structured Evolution with Compact Architectures for Scalable Policy Optimization

Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard Turner, Adrian Weller ; PMLR 80:970-978

Path Consistency Learning in Tsallis Entropy Regularized MDPs

Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh ; PMLR 80:979-988

An Iterative, Sketching-based Framework for Ridge Regression

Agniva Chowdhury, Jiasen Yang, Petros Drineas ; PMLR 80:989-998

Stochastic Wasserstein Barycenters

Sebastian Claici, Edward Chien, Justin Solomon ; PMLR 80:999-1008

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

John Co-Reyes, YuXuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine ; PMLR 80:1009-1018

On Acceleration with Noise-Corrupted Gradients

Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia ; PMLR 80:1019-1028

Online Linear Quadratic Control

Alon Cohen, Avinatan Hasidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar ; PMLR 80:1029-1038

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

Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer ; PMLR 80:1039-1048

Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation

Dane Corneil, Wulfram Gerstner, Johanni Brea ; PMLR 80:1049-1058

Online Learning with Abstention

Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang ; PMLR 80:1059-1067

Constrained Interacting Submodular Groupings

Andrew Cotter, Mahdi Milani Fard, Seungil You, Maya Gupta, Jeff Bilmes ; PMLR 80:1068-1077

Inference Suboptimality in Variational Autoencoders

Chris Cremer, Xuechen Li, David Duvenaud ; PMLR 80:1078-1086

Mix & Match Agent Curricula for Reinforcement Learning

Wojciech Czarnecki, Siddhant Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu ; PMLR 80:1087-1095

Implicit Quantile Networks for Distributional Reinforcement Learning

Will Dabney, Georg Ostrovski, David Silver, Remi Munos ; PMLR 80:1096-1105

Learning Steady-States of Iterative Algorithms over Graphs

Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song ; PMLR 80:1106-1114

Adversarial Attack on Graph Structured Data

Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song ; PMLR 80:1115-1124

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 80:1125-1134

Compressing Neural Networks using the Variational Information Bottleneck

Bin Dai, Chen Zhu, Baining Guo, David Wipf ; PMLR 80:1135-1144

Asynchronous Byzantine Machine Learning (the case of SGD)

Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Rhicheek Patra, Mahsa Taziki ; PMLR 80:1145-1154

Escaping Saddles with Stochastic Gradients

Hadi Daneshmand, Jonas Kohler, Aurelien Lucchi, Thomas Hofmann ; PMLR 80:1155-1164

Minibatch Gibbs Sampling on Large Graphical Models

Chris De Sa, Vincent Chen, Wing Wong ; PMLR 80:1165-1173

Stochastic Video Generation with a Learned Prior

Emily Denton, Rob Fergus ; PMLR 80:1174-1183

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 80:1184-1193

Accurate Inference for Adaptive Linear Models

Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy ; PMLR 80:1194-1203

Variational Network Inference: Strong and Stable with Concrete Support

Amir Dezfouli, Edwin Bonilla, Richard Nock ; PMLR 80:1204-1213

Modeling Sparse Deviations for Compressed Sensing using Generative Models

Manik Dhar, Aditya Grover, Stefano Ermon ; PMLR 80:1214-1223

Alternating Randomized Block Coordinate Descent

Jelena Diakonikolas, Lorenzo Orecchia ; PMLR 80:1224-1232

Learning to Act in Decentralized Partially Observable MDPs

Jilles Dibangoye, Olivier Buffet ; PMLR 80:1233-1242

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

Charlie Dickens, Graham Cormode, David Woodruff ; PMLR 80:1243-1251

Noisin: Unbiased Regularization for Recurrent Neural Networks

Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David Blei ; PMLR 80:1252-1261

Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning

Thomas Dietterich, George Trimponias, Zhitang Chen ; PMLR 80:1262-1270

Coordinated Exploration in Concurrent Reinforcement Learning

Maria Dimakopoulou, Benjamin Van Roy ; PMLR 80:1271-1279

Probabilistic Recurrent State-Space Models

Andreas Doerr, Christian Daniel, Martin Schiegg, Nguyen-Tuong Duy, Stefan Schaal, Marc Toussaint, Trimpe Sebastian ; PMLR 80:1280-1289

Randomized Block Cubic Newton Method

Nikita Doikov, Peter Richtarik, University Edinburgh ; PMLR 80:1290-1298

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

Ahmed Douik, Babak Hassibi ; PMLR 80:1299-1308

Essentially No Barriers in Neural Network Energy Landscape

Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred Hamprecht ; PMLR 80:1309-1318

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

Alexey Drutsa ; PMLR 80:1319-1328

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

Simon Du, Jason Lee ; PMLR 80:1329-1338

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

Simon Du, Jason Lee, Yuandong Tian, Aarti Singh, Barnabas Poczos ; PMLR 80:1339-1348

Investigating Human Priors for Playing Video Games

Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei Efros ; PMLR 80:1349-1357

A Distributed Second-Order Algorithm You Can Trust

Celestine Duenner, Aurelien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi ; PMLR 80:1358-1366

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

Pavel Dvurechensky, Alexander Gasnikov, Alexey Kroshnin ; PMLR 80:1367-1376

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 80:1377-1386

Beyond the One-Step Greedy Approach in Reinforcement Learning

Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor ; PMLR 80:1387-1396

Parallel and Streaming Algorithms for K-Core Decomposition

Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni ; PMLR 80:1397-1406

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

Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu ; PMLR 80:1407-1416

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

Trefor Evans, Prasanth Nair ; PMLR 80:1417-1426

The Limits of Maxing, Ranking, and Preference Learning

Moein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar ; PMLR 80:1427-1436

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

Stefan Falkner, Aaron Klein, Frank Hutter ; PMLR 80:1437-1446

More Robust Doubly Robust Off-policy Evaluation

Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh ; PMLR 80:1447-1456

Efficient and Consistent Adversarial Bipartite Matching

Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian Ziebart ; PMLR 80:1457-1466

Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator

Maryam Fazel, Rong Ge, Sham Kakade, Mehran Mesbahi ; PMLR 80:1467-1476

CRVI: Convex Relaxation for Variational Inference

Ghazal Fazelnia, John Paisley ; PMLR 80:1477-1485

Fourier Policy Gradients

Matthew Fellows, Kamil Ciosek, Shimon Whiteson ; PMLR 80:1486-1495

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

Jean Feng, Brian Williamson, Noah Simon, Marco Carone ; PMLR 80:1496-1505

Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization

Louis Filstroff, Alberto Lumbreras, Cédric Févotte ; PMLR 80:1506-1514

Automatic Goal Generation for Reinforcement Learning Agents

Carlos Florensa, David Held, Xinyang Geng, Pieter Abbeel ; PMLR 80:1515-1528

DiCE: The Infinitely Differentiable Monte Carlo Estimator

Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric Xing, Shimon Whiteson ; PMLR 80:1529-1538

Practical Contextual Bandits with Regression Oracles

Dylan Foster, Alekh Agarwal, Miroslav Dudik, Haipeng Luo, Robert Schapire ; PMLR 80:1539-1548

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 80:1549-1558

ADMM and Accelerated ADMM as Continuous Dynamical Systems

Guilherme Franca, Daniel Robinson, Rene Vidal ; PMLR 80:1559-1567

Bilevel Programming for Hyperparameter Optimization and Meta-Learning

Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimiliano Pontil ; PMLR 80:1568-1577

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

Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner ; PMLR 80:1578-1586

Addressing Function Approximation Error in Actor-Critic Methods

Scott Fujimoto, Herke Hoof, David Meger ; PMLR 80:1587-1596

Clipped Action Policy Gradient

Yasuhiro Fujita, Shin-ichi Maeda ; PMLR 80:1597-1606

Born Again Neural Networks

Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar ; PMLR 80:1607-1616

The Generalization Error of Dictionary Learning with Moreau Envelopes

Alexandros Georgogiannis ; PMLR 80:1617-1625

Local Private Hypothesis Testing: Chi-Square Tests

Marco Gaboardi, Ryan Rogers ; PMLR 80:1626-1635

Inductive Two-Layer Modeling with Parametric Bregman Transfer

Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu ; PMLR 80:1636-1645

Hyperbolic Entailment Cones for Learning Hierarchical Embeddings

Octavian Ganea, Gary Becigneul, Thomas Hofmann ; PMLR 80:1646-1655

Parameterized Algorithms for the Matrix Completion Problem

Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider ; PMLR 80:1656-1665

Synthesizing Programs for Images using Reinforced Adversarial Learning

Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S. M. Ali Eslami, Oriol Vinyals ; PMLR 80:1666-1675

Spotlight: Optimizing Device Placement for Training Deep Neural Networks

Yuanxiang Gao, Li Chen, Baochun Li ; PMLR 80:1676-1684

Parallel Bayesian Network Structure Learning

Tian Gao, Dennis Wei ; PMLR 80:1685-1694

Structured Output Learning with Abstention: Application to Accurate Opinion Prediction

Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d’Alche-Buc ; PMLR 80:1695-1703

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 80:1704-1713

Temporal Poisson Square Root Graphical Models

Sinong Geng, Zhaobin Kuang, Peggy Peissig, David Page ; PMLR 80:1714-1723

Budgeted Experiment Design for Causal Structure Learning

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim ; PMLR 80:1724-1733

Linear Spectral Estimators and an Application to Phase Retrieval

Ramina Ghods, Andrew Lan, Tom Goldstein, Christoph Studer ; PMLR 80:1734-1743

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez ; PMLR 80:1744-1753

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

Asish Ghoshal, Jean Honorio ; PMLR 80:1754-1762

Robust and Scalable Models of Microbiome Dynamics

Travis Gibson, Georg Gerber ; PMLR 80:1763-1772

Non-linear motor control by local learning in spiking neural networks

Aditya Gilra, Wulfram Gerstner ; PMLR 80:1773-1782

Learning One Convolutional Layer with Overlapping Patches

Surbhi Goel, Adam Klivans, Raghu Meka ; PMLR 80:1783-1791

Visualizing and Understanding Atari Agents

Samuel Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern ; PMLR 80:1792-1801

Learning Policy Representations in Multiagent Systems

Aditya Grover, Maruan Al-Shedivat, Jayesh Gupta, Yuri Burda, Harrison Edwards ; PMLR 80:1802-1811

Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines

Bin Gu, Zhouyuan Huo, Cheng Deng, Heng Huang ; PMLR 80:1812-1821

Learning to search with MCTSnets

Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Remi Munos, David Silver ; PMLR 80:1822-1831

Characterizing Implicit Bias in Terms of Optimization Geometry

Suriya Gunasekar, Jason Lee, Daniel Soudry, Nathan Srebro ; PMLR 80:1832-1841

Shampoo: Preconditioned Stochastic Tensor Optimization

Vineet Gupta, Tomer Koren, Yoram Singer ; PMLR 80:1842-1850

Latent Space Policies for Hierarchical Reinforcement Learning

Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine ; PMLR 80:1851-1860

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

Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine ; PMLR 80:1861-1870

Comparison-Based Random Forests

Siavash Haghiri, Damien Garreau, Ulrike Luxburg ; PMLR 80:1871-1880

K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning

Jihun Hamm, Yung-Kyun Noh ; PMLR 80:1881-1889

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

Lei Han, Yiheng Huang, Tong Zhang ; PMLR 80:1890-1899

Stein Variational Gradient Descent Without Gradient

Jun Han, Qiang Liu ; PMLR 80:1900-1908

Deep Models of Interactions Across Sets

Jason Hartford, Devon Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh ; PMLR 80:1909-1918

Learning Memory Access Patterns

Milad Hashemi, Kevin Swersky, Jamie Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan ; PMLR 80:1919-1928

Fairness Without Demographics in Repeated Loss Minimization

Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang ; PMLR 80:1929-1938

Multicalibration: Calibration for the (Computationally-Identifiable) Masses

Ursula Hebert-Johnson, Michael Kim, Omer Reingold, Guy Rothblum ; PMLR 80:1939-1948

Recurrent Predictive State Policy Networks

Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha Srinivasa, Geoffrey Gordon ; PMLR 80:1949-1958

Learning unknown ODE models with Gaussian processes

Markus Heinonen, Cagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki ; PMLR 80:1959-1968

Orthogonal Recurrent Neural Networks with Scaled Cayley Transform

Kyle Helfrich, Devin Willmott, Qiang Ye ; PMLR 80:1969-1978

Fast Bellman Updates for Robust MDPs

Chin Pang Ho, Marek Petrik, Wolfram Wiesemann ; PMLR 80:1979-1988

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, Trevor Darrell ; PMLR 80:1989-1998

Sound Abstraction and Decomposition of Probabilistic Programs

Steven Holtzen, Guy Broeck, Todd Millstein ; PMLR 80:1999-2008

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

Mingyi Hong, Meisam Razaviyayn, Jason Lee ; PMLR 80:2009-2018

Variational Bayesian dropout: pitfalls and fixes

Jiri Hron, Alex Matthews, Zoubin Ghahramani ; PMLR 80:2019-2028

Does Distributionally Robust Supervised Learning Give Robust Classifiers?

Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama ; PMLR 80:2029-2037

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

Bin Hu, Stephen Wright, Laurent Lessard ; PMLR 80:2038-2047

Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices

Zengfeng Huang ; PMLR 80:2048-2057

Learning Deep ResNet Blocks Sequentially using Boosting Theory

Furong Huang, Jordan Ash, John Langford, Robert Schapire ; PMLR 80:2058-2067

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

Kejun Huang, Xiao Fu, Nicholas Sidiropoulos ; PMLR 80:2068-2077

Neural Autoregressive Flows

Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville ; PMLR 80:2078-2087

Topological mixture estimation

Steve Huntsman ; PMLR 80:2088-2097

Decoupled Parallel Backpropagation with Convergence Guarantee

Zhouyuan Huo, Bin Gu, Yang, Heng Huang ; PMLR 80:2098-2106

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

Rodrigo Toro Icarte, Toryn Klassen, Richard Valenzano, Sheila McIlraith ; PMLR 80:2107-2116

Deep Variational Reinforcement Learning for POMDPs

Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson ; PMLR 80:2117-2126

Attention-based Deep Multiple Instance Learning

Maximilian Ilse, Jakub Tomczak, Max Welling ; PMLR 80:2127-2136

Black-box Adversarial Attacks with Limited Queries and Information

Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin ; PMLR 80:2137-2146

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

Hideaki Imamura, Issei Sato, Masashi Sugiyama ; PMLR 80:2147-2156

Improving Regression Performance with Distributional Losses

Ehsan Imani, Martha White ; PMLR 80:2157-2166

Deep Density Destructors

David Inouye, Pradeep Ravikumar ; PMLR 80:2167-2175

Unbiased Objective Estimation in Predictive Optimization

Shinji Ito, Akihiro Yabe, Ryohei Fujimaki ; PMLR 80:2176-2185

Anonymous Walk Embeddings

Sergey Ivanov, Evgeny Burnaev ; PMLR 80:2186-2195

Learning Binary Latent Variable Models: A Tensor Eigenpair Approach

Ariel Jaffe, Roi Weiss, Boaz Nadler, Shai Carmi, Yuval Kluger ; PMLR 80:2196-2205

Firing Bandits: Optimizing Crowdfunding

Lalit Jain, Kevin Jamieson ; PMLR 80:2206-2214

Differentially Private Matrix Completion Revisited

Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta ; PMLR 80:2215-2224

Video Prediction with Appearance and Motion Conditions

Yunseok Jang, Gunhee Kim, Yale Song ; PMLR 80:2225-2234

Pathwise Derivatives Beyond the Reparameterization Trick

Martin Jankowiak, Fritz Obermeyer ; PMLR 80:2235-2244

Detecting non-causal artifacts in multivariate linear regression models

Dominik Janzing, Bernhard Schölkopf ; PMLR 80:2245-2253

A Unified Framework for Structured Low-rank Matrix Learning

Pratik Jawanpuria, Bamdev Mishra ; PMLR 80:2254-2263

Efficient end-to-end learning for quantizable representations

Yeonwoo Jeong, Hyun Oh Song ; PMLR 80:2264-2273

Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks

Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken ; PMLR 80:2274-2283

Feedback-Based Tree Search for Reinforcement Learning

Daniel Jiang, Emmanuel Ekwedike, Han Liu ; PMLR 80:2284-2293

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

Heinrich Jiang, Jennifer Jang, Samory Kpotufe ; PMLR 80:2294-2303

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 80:2304-2313

The Weighted Kendall and High-order Kernels for Permutations

Yunlong Jiao, Jean-Philippe Vert ; PMLR 80:2314-2322

Junction Tree Variational Autoencoder for Molecular Graph Generation

Wengong Jin, Regina Barzilay, Tommi Jaakkola ; PMLR 80:2323-2332

Network Global Testing by Counting Graphlets

Jiashun Jin, Zheng Ke, Shengming Luo ; PMLR 80:2333-2341

Regret Minimization for Partially Observable Deep Reinforcement Learning

Peter Jin, Kurt Keutzer, Sergey Levine ; PMLR 80:2342-2351

WSNet: Compact and Efficient Networks Through Weight Sampling

Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic, Jiashi Feng, Shuicheng Yan ; PMLR 80:2352-2361

Large-Scale Cox Process Inference using Variational Fourier Features

ST John, James Hensman ; PMLR 80:2362-2370

Composite Functional Gradient Learning of Generative Adversarial Models

Rie Johnson, Tong Zhang ; PMLR 80:2371-2379

Kronecker Recurrent Units

Cijo Jose, Moustapha Cisse, Francois Fleuret ; PMLR 80:2380-2389

Fast Decoding in Sequence Models Using Discrete Latent Variables

Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, Noam Shazeer ; PMLR 80:2390-2399

Kernel Recursive ABC: Point Estimation with Intractable Likelihood

Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu ; PMLR 80:2400-2409

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 80:2410-2419

Learning Diffusion using Hyperparameters

Dimitris Kalimeris, Yaron Singer, Karthik Subbian, Udi Weinsberg ; PMLR 80:2420-2428

Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit

Sreejith Kallummil, Sheetal Kalyani ; PMLR 80:2429-2438

Residual Unfairness in Fair Machine Learning from Prejudiced Data

Nathan Kallus, Angela Zhou ; PMLR 80:2439-2448

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

Ashwin Kalyan, Stefan Lee, Anitha Kannan, Dhruv Batra ; PMLR 80:2449-2458

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 80:2459-2468

Policy Optimization with Demonstrations

Bingyi Kang, Zequn Jie, Jiashi Feng ; PMLR 80:2469-2478

Improving Sign Random Projections With Additional Information

Keegan Kang, Weipin Wong ; PMLR 80:2479-2487

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 80:2488-2496

Continual Reinforcement Learning with Complex Synapses

Christos Kaplanis, Murray Shanahan, Claudia Clopath ; PMLR 80:2497-2506

LaVAN: Localized and Visible Adversarial Noise

Danny Karmon, Daniel Zoran, Yoav Goldberg ; PMLR 80:2507-2515

Riemannian Stochastic Recursive Gradient Algorithm

Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra ; PMLR 80:2516-2524

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

Angelos Katharopoulos, Francois Fleuret ; PMLR 80:2525-2534

Feasible Arm Identification

Julian Katz-Samuels, Clay Scott ; PMLR 80:2535-2543

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

Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi ; PMLR 80:2544-2553

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 80:2554-2563

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu ; PMLR 80:2564-2572

Improved nearest neighbor search using auxiliary information and priority functions

Omid Keivani, Kaushik Sinha ; PMLR 80:2573-2581

ContextNet: Deep learning for Star Galaxy Classification

Noble Kennamer, David Kirkby, Alexander Ihler, Francisco Javier Sanchez-Lopez ; PMLR 80:2582-2590

Frank-Wolfe with Subsampling Oracle

Thomas Kerdreux, Fabian Pedregosa, Alexandre d’Aspremont ; PMLR 80:2591-2600

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

Koulik Khamaru, Martin Wainwright ; PMLR 80:2601-2610

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

Mohammad Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava ; PMLR 80:2611-2620

Geometry Score: A Method For Comparing Generative Adversarial Networks

Valentin Khrulkov, Ivan Oseledets ; PMLR 80:2621-2629

Blind Justice: Fairness with Encrypted Sensitive Attributes

Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna Gummadi, Adrian Weller ; PMLR 80:2630-2639

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

Minyoung Kim ; PMLR 80:2640-2648

Disentangling by Factorising

Hyunjik Kim, Andriy Mnih ; PMLR 80:2649-2658

Self-Bounded Prediction Suffix Tree via Approximate String Matching

Dongwoo Kim, Christian Walder ; PMLR 80:2659-2667

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 80:2668-2677

Semi-Amortized Variational Autoencoders

Yoon Kim, Sam Wiseman, Andrew Miller, David Sontag, Alexander Rush ; PMLR 80:2678-2687

Neural Relational Inference for Interacting Systems

Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel ; PMLR 80:2688-2697

An Alternative View: When Does SGD Escape Local Minima?

Bobby Kleinberg, Yuanzhi Li, Yang Yuan ; PMLR 80:2698-2707

Crowdsourcing with Arbitrary Adversaries

Matthaeus Kleindessner, Pranjal Awasthi ; PMLR 80:2708-2717

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

Jeremias Knoblauch, Theodoros Damoulas ; PMLR 80:2718-2727

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

Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamas Keviczky ; PMLR 80:2728-2736

Nonconvex Optimization for Regression with Fairness Constraints

Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao ; PMLR 80:2737-2746

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

Risi Kondor, Shubhendu Trivedi ; PMLR 80:2747-2755

Compiling Combinatorial Prediction Games

Frederic Koriche ; PMLR 80:2756-2765

Dynamic Evaluation of Neural Sequence Models

Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals ; PMLR 80:2766-2775

Semiparametric Contextual Bandits

Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis ; PMLR 80:2776-2785

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

Alan Kuhnle, J. David Smith, Victoria Crawford, My Thai ; PMLR 80:2786-2795

Accurate Uncertainties for Deep Learning Using Calibrated Regression

Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon ; PMLR 80:2796-2804

Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings

Aviral Kumar, Sunita Sarawagi, Ujjwal Jain ; PMLR 80:2805-2814

Data-Dependent Stability of Stochastic Gradient Descent

Ilja Kuzborskij, Christoph Lampert ; PMLR 80:2815-2824

Explicit Inductive Bias for Transfer Learning with Convolutional Networks

Xuhong LI, Yves Grandvalet, Franck Davoine ; PMLR 80:2825-2834

Understanding the Loss Surface of Neural Networks for Binary Classification

SHIYU LIANG, Ruoyu Sun, Yixuan Li, Rayadurgam Srikant ; PMLR 80:2835-2843

Mixed batches and symmetric discriminators for GAN training

Thomas LUCAS, Corentin Tallec, Yann Ollivier, Jakob Verbeek ; PMLR 80:2844-2853

Binary Partitions with Approximate Minimum Impurity

Eduardo Laber, Marco Molinaro, Felipe Mello Pereira ; PMLR 80:2854-2862

Canonical Tensor Decomposition for Knowledge Base Completion

Timothee Lacroix, Nicolas Usunier, Guillaume Obozinski ; PMLR 80:2863-2872

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

Brenden Lake, Marco Baroni ; PMLR 80:2873-2882

An Estimation and Analysis Framework for the Rasch Model

Andrew Lan, Mung Chiang, Christoph Studer ; PMLR 80:2883-2891

Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering

Jan-Hendrik Lange, Andreas Karrenbauer, Bjoern Andres ; PMLR 80:2892-2901

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

Thomas Laurent, James Brecht ; PMLR 80:2902-2907

The Multilinear Structure of ReLU Networks

Thomas Laurent, James Brecht ; PMLR 80:2908-2916

Hierarchical Imitation and Reinforcement Learning

Hoang Le, Nan Jiang, Alekh Agarwal, Miroslav Dudik, Yisong Yue, Hal Daumé ; PMLR 80:2917-2926

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

Yoonho Lee, Seungjin Choi ; PMLR 80:2927-2936

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 80:2937-2946

Gated Path Planning Networks

Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov ; PMLR 80:2947-2955

Deep Asymmetric Multi-task Feature Learning

Hae Beom Lee, Eunho Yang, Sung Ju Hwang ; PMLR 80:2956-2964

Noise2Noise: Learning Image Restoration without Clean Data

Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila ; PMLR 80:2965-2974

Out-of-sample extension of graph adjacency spectral embedding

Keith Levin, Fred Roosta, Michael Mahoney, Carey Priebe ; PMLR 80:2975-2984

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

Qianxiao Li, Shuji Hao ; PMLR 80:2985-2994

Towards Binary-Valued Gates for Robust LSTM Training

Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, Tieyan Liu ; PMLR 80:2995-3004

On the Limitations of First-Order Approximation in GAN Dynamics

Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt ; PMLR 80:3005-3013

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

Pan Li, Olgica Milenkovic ; PMLR 80:3014-3023

The Well-Tempered Lasso

Yuanzhi Li, Yoram Singer ; PMLR 80:3024-3032

Estimation of Markov Chain via Rank-Constrained Likelihood

Xudong Li, Mengdi Wang, Anru Zhang ; PMLR 80:3033-3042

Asynchronous Decentralized Parallel Stochastic Gradient Descent

Xiangru Lian, Wei Zhang, Ce Zhang, Ji Liu ; PMLR 80:3043-3052

RLlib: Abstractions for Distributed Reinforcement Learning

Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, Ion Stoica ; PMLR 80:3053-3062

On the Spectrum of Random Features Maps of High Dimensional Data

Zhenyu Liao, Romain Couillet ; PMLR 80:3063-3071

The Dynamics of Learning: A Random Matrix Approach

Zhenyu Liao, Romain Couillet ; PMLR 80:3072-3081

Reviving and Improving Recurrent Back-Propagation

Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel ; PMLR 80:3082-3091

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Junhong Lin, Volkan Cevher ; PMLR 80:3092-3101

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

Junhong Lin, Volkan Cevher ; PMLR 80:3102-3111

Level-Set Methods for Finite-Sum Constrained Convex Optimization

Qihang Lin, Runchao Ma, Tianbao Yang ; PMLR 80:3112-3121

Detecting and Correcting for Label Shift with Black Box Predictors

Zachary Lipton, Yu-Xiang Wang, Alexander Smola ; PMLR 80:3122-3130

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

Haitao Liu, Jianfei Cai, Yi Wang, Yew Soon Ong ; PMLR 80:3131-3140

Towards Black-box Iterative Machine Teaching

Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James Rehg, Le Song ; PMLR 80:3141-3149

Delayed Impact of Fair Machine Learning

Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt ; PMLR 80:3150-3158

A Two-Step Computation of the Exact GAN Wasserstein Distance

Huidong Liu, Xianfeng GU, Dimitris Samaras ; PMLR 80:3159-3168

Open Category Detection with PAC Guarantees

Si Liu, Risheek Garrepalli, Thomas Dietterich, Alan Fern, Dan Hendrycks ; PMLR 80:3169-3178

Fast Variance Reduction Method with Stochastic Batch Size

Xuanqing Liu, Cho-Jui Hsieh ; PMLR 80:3179-3188

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

Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang ; PMLR 80:3189-3197

On Matching Pursuit and Coordinate Descent

Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Raetsch, Bernhard Schölkopf, Sebastian Stich, Martin Jaggi ; PMLR 80:3198-3207

PDE-Net: Learning PDEs from Data

Zichao Long, Yiping Lu, Xianzhong Ma, Bin Dong ; PMLR 80:3208-3216

Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap

Miles Lopes, Shusen Wang, Michael Mahoney ; PMLR 80:3217-3226

Constraining the Dynamics of Deep Probabilistic Models

Marco Lorenzi, Maurizio Filippone ; PMLR 80:3227-3236

Spectrally Approximating Large Graphs with Smaller Graphs

Andreas Loukas, Pierre Vandergheynst ; PMLR 80:3237-3246

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

Hao Lu, Yuan Cao, Zhuoran Yang, Junwei Lu, Han Liu, Zhaoran Wang ; PMLR 80:3247-3256

Accelerating Greedy Coordinate Descent Methods

Haihao Lu, Robert Freund, Vahab Mirrokni ; PMLR 80:3257-3266

Structured Variationally Auto-encoded Optimization

Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai, Neil Lawrence ; PMLR 80:3267-3275

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

Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong ; PMLR 80:3276-3285

End-to-end Active Object Tracking via Reinforcement Learning

Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang ; PMLR 80:3286-3295

Competitive Caching with Machine Learned Advice

Thodoris Lykouris, Sergei Vassilvtiskii ; PMLR 80:3296-3305

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

Wenlong Lyu, Fan Yang, Changhao Yan, Dian Zhou, Xuan Zeng ; PMLR 80:3306-3314

Celer: a Fast Solver for the Lasso with Dual Extrapolation

Mathurin MASSIAS, Alexandre Gramfort, Joseph Salmon ; PMLR 80:3315-3324

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

Siyuan Ma, Raef Bassily, Mikhail Belkin ; PMLR 80:3325-3334

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 80:3335-3344

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 80:3345-3354

Dimensionality-Driven Learning with Noisy Labels

Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, James Bailey ; PMLR 80:3355-3364

Approximate message passing for amplitude based optimization

Junjie Ma, Ji Xu, Arian Maleki ; PMLR 80:3365-3374

Orthogonal Machine Learning: Power and Limitations

Lester Mackey, Vasilis Syrgkanis, Ilias Zadik ; PMLR 80:3375-3383

Learning Adversarially Fair and Transferable Representations

David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel ; PMLR 80:3384-3393

An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning

Dhruv Malik, Malayandi Palaniappan, Jaime Fisac, Dylan Hadfield-Menell, Stuart Russell, Anca Dragan ; PMLR 80:3394-3402

Iterative Amortized Inference

Joe Marino, Yisong Yue, Stephan Mandt ; PMLR 80:3403-3412

Streaming Principal Component Analysis in Noisy Setting

Teodor Vanislavov Marinov, Poorya Mianjy, Raman Arora ; PMLR 80:3413-3422

Fast Approximate Spectral Clustering for Dynamic Networks

Lionel Martin, Andreas Loukas, Pierre Vandergheynst ; PMLR 80:3423-3432

Bayesian Model Selection for Change Point Detection and Clustering

Othmane Mazhar, Cristian Rojas, Carlo Fischione, Mohammad Reza Hesamzadeh ; PMLR 80:3433-3442

Optimization, fast and slow: optimally switching between local and Bayesian optimization

Mark McLeod, Stephen Roberts, Michael A. Osborne ; PMLR 80:3443-3452

Bounds on the Approximation Power of Feedforward Neural Networks

Mohammad Mehrabi, Aslan Tchamkerten, MANSOOR YOUSEFI ; PMLR 80:3453-3461

Differentiable Dynamic Programming for Structured Prediction and Attention

Arthur Mensch, Mathieu Blondel ; PMLR 80:3462-3471

Ranking Distributions based on Noisy Sorting

Adil El Mesaoudi-Paul, Eyke Hüllermeier, Robert Busa-Fekete ; PMLR 80:3472-3480

Which Training Methods for GANs do actually Converge?

Lars Mescheder, Andreas Geiger, Sebastian Nowozin ; PMLR 80:3481-3490

Configurable Markov Decision Processes

Alberto Maria Metelli, Mirco Mutti, Marcello Restelli ; PMLR 80:3491-3500

prDeep: Robust Phase Retrieval with a Flexible Deep Network

Christopher Metzler, Phillip Schniter, Ashok Veeraraghavan, baraniuk ; PMLR 80:3501-3510

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

Elliot Meyerson, Risto Miikkulainen ; PMLR 80:3511-3520

The Hidden Vulnerability of Distributed Learning in Byzantium

El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault ; PMLR 80:3521-3530

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

Poorya Mianjy, Raman Arora ; PMLR 80:3531-3539

On the Implicit Bias of Dropout

Poorya Mianjy, Raman Arora, Rene Vidal ; PMLR 80:3540-3548

One-Shot Segmentation in Clutter

Claudio Michaelis, Matthias Bethge, Alexander Ecker ; PMLR 80:3549-3558

Differentiable plasticity: training plastic neural networks with backpropagation

Thomas Miconi, Kenneth Stanley, Jeff Clune ; PMLR 80:3559-3568

Training Neural Machines with Trace-Based Supervision

Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevic, Timon Gehr, Martin Vechev ; PMLR 80:3569-3577

Differentiable Abstract Interpretation for Provably Robust Neural Networks

Matthew Mirman, Timon Gehr, Martin Vechev ; PMLR 80:3578-3586

A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning

Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini ; PMLR 80:3587-3595

Data Summarization at Scale: A Two-Stage Submodular Approach

Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi ; PMLR 80:3596-3605

The Hierarchical Adaptive Forgetting Variational Filter

Vincent Moens ; PMLR 80:3606-3615

Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings

Aryan Mokhtari, Hamed Hassani, Amin Karbasi ; PMLR 80:3616-3625

DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding

Thomas Moreau, Laurent Oudre, Nicolas Vayatis ; PMLR 80:3626-3634

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

Marine Le Morvan, Jean-Philippe Vert ; PMLR 80:3635-3644

Dropout Training, Data-dependent Regularization, and Generalization Bounds

Wenlong Mou, Yuchen Zhou, Jun Gao, Liwei Wang ; PMLR 80:3645-3653

Kernelized Synaptic Weight Matrices

Lorenz Muller, Julien Martel, Giacomo Indiveri ; PMLR 80:3654-3663

Rapid Adaptation with Conditionally Shifted Neurons

Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri, Adam Trischler ; PMLR 80:3664-3673

On the Relationship between Data Efficiency and Error for Uncertainty Sampling

Stephen Mussmann, Percy Liang ; PMLR 80:3674-3682

Fitting New Speakers Based on a Short Untranscribed Sample

Eliya Nachmani, Adam Polyak, Yaniv Taigman, Lior Wolf ; PMLR 80:3683-3691

Smoothed Action Value Functions for Learning Gaussian Policies

Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans ; PMLR 80:3692-3700

Nearly Optimal Robust Subspace Tracking

Praneeth Narayanamurthy, Namrata Vaswani ; PMLR 80:3701-3709

Stochastic Proximal Algorithms for AUC Maximization

Michael Natole, Yiming Ying, Siwei Lyu ; PMLR 80:3710-3719

Mitigating Bias in Adaptive Data Gathering via Differential Privacy

Seth Neel, Aaron Roth ; PMLR 80:3720-3729

Optimization Landscape and Expressivity of Deep CNNs

Quynh Nguyen, Matthias Hein ; PMLR 80:3730-3739

Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions

Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein ; PMLR 80:3740-3749

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

Lam Nguyen, PHUONG HA NGUYEN, Marten Dijk, Peter Richtarik, Katya Scheinberg, Martin Takac ; PMLR 80:3750-3758

Active Testing: An Efficient and Robust Framework for Estimating Accuracy

Phuc Nguyen, Deva Ramanan, Charless Fowlkes ; PMLR 80:3759-3768

On Learning Sparsely Used Dictionaries from Incomplete Samples

Thanh Nguyen, Akshay Soni, Chinmay Hegde ; PMLR 80:3769-3778

Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry

Maximillian Nickel, Douwe Kiela ; PMLR 80:3779-3788

State Space Gaussian Processes with Non-Gaussian Likelihood

Hannes Nickisch, Arno Solin, Alexander Grigorevskiy ; PMLR 80:3789-3798

SparseMAP: Differentiable Sparse Structured Inference

Vlad Niculae, Andre Martins, Mathieu Blondel, Claire Cardie ; PMLR 80:3799-3808

A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations

Weili Nie, Yang Zhang, Ankit Patel ; PMLR 80:3809-3818

Functional Gradient Boosting based on Residual Network Perception

Atsushi Nitanda, Taiji Suzuki ; PMLR 80:3819-3828

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 80:3829-3838

The Uncertainty Bellman Equation and Exploration

Brendan O’Donoghue, Ian Osband, Remi Munos, Vlad Mnih ; PMLR 80:3839-3848

Is Generator Conditioning Causally Related to GAN Performance?

Augustus Odena, Jacob Buckman, Catherine Olsson, Tom Brown, Christopher Olah, Colin Raffel, Ian Goodfellow ; PMLR 80:3849-3858

Learning in Reproducing Kernel Kreı̆n Spaces

Dino Oglic, Thomas Gaertner ; PMLR 80:3859-3867

BOCK : Bayesian Optimization with Cylindrical Kernels

ChangYong Oh, Efstratios Gavves, Max Welling ; PMLR 80:3868-3877

Self-Imitation Learning

Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee ; PMLR 80:3878-3887

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

Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira ; PMLR 80:3888-3897

Transformation Autoregressive Networks

Junier Oliva, Avinava Dubey, Manzil Zaheer, Barnabas Poczos, Ruslan Salakhutdinov, Eric Xing, Jeff Schneider ; PMLR 80:3898-3907

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

Simon Olofsson, Marc Deisenroth, Ruth Misener ; PMLR 80:3908-3917

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 80:3918-3926

Learning Localized Spatio-Temporal Models From Streaming Data

Muhammad Osama, Dave Zachariah, Thomas Schön ; PMLR 80:3927-3935

Autoregressive Quantile Networks for Generative Modeling

Georg Ostrovski, Will Dabney, Remi Munos ; PMLR 80:3936-3945

Efficient First-Order Algorithms for Adaptive Signal Denoising

Dmitrii Ostrovskii, Zaid Harchaoui ; PMLR 80:3946-3955

Analyzing Uncertainty in Neural Machine Translation

Myle Ott, Michael Auli, David Grangier, Marc’Aurelio Ranzato ; PMLR 80:3956-3965

Learning Compact Neural Networks with Regularization

Samet Oymak ; PMLR 80:3966-3975

Tree Edit Distance Learning via Adaptive Symbol Embeddings

Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Barbara Hammer ; PMLR 80:3976-3985

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 80:3986-3995

Learning to Speed Up Structured Output Prediction

Xingyuan Pan, Vivek Srikumar ; PMLR 80:3996-4005

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

Xudong Pan, Mi Zhang, Daizong Ding ; PMLR 80:4006-4015

Max-Mahalanobis Linear Discriminant Analysis Networks

Tianyu Pang, Chao Du, Jun Zhu ; PMLR 80:4016-4025

Stochastic Variance-Reduced Policy Gradient

Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli ; PMLR 80:4026-4035

Learning Independent Causal Mechanisms

Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf ; PMLR 80:4036-4044

Time Limits in Reinforcement Learning

Fabio Pardo, Arash Tavakoli, Vitaly Levdik, Petar Kormushev ; PMLR 80:4045-4054

Image Transformer

Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran ; PMLR 80:4055-4064

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

Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya ; PMLR 80:4065-4074

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

Tim Pearce, Alexandra Brintrup, Mohamed Zaki, Andy Neely ; PMLR 80:4075-4084

Adaptive Three Operator Splitting

Fabian Pedregosa, Gauthier Gidel ; PMLR 80:4085-4094

Efficient Neural Architecture Search via Parameters Sharing

Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, Jeff Dean ; PMLR 80:4095-4104

Bandits with Delayed, Aggregated Anonymous Feedback

Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvari, Steffen Grunewalder ; PMLR 80:4105-4113

Constant-Time Predictive Distributions for Gaussian Processes

Geoff Pleiss, Jacob Gardner, Kilian Weinberger, Andrew Gordon Wilson ; PMLR 80:4114-4123

Local Convergence Properties of SAGA/Prox-SVRG and Acceleration

Clarice Poon, Jingwei Liang, Carola Schoenlieb ; PMLR 80:4124-4132

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

Guillaume Pouliot ; PMLR 80:4133-4140

Learning Dynamics of Linear Denoising Autoencoders

Arnu Pretorius, Steve Kroon, Herman Kamper ; PMLR 80:4141-4150

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 Duke ; PMLR 80:4151-4160

Selecting Representative Examples for Program Synthesis

Yewen Pu, Zachery Miranda, Armando Solar-Lezama, Leslie Kaelbling ; PMLR 80:4161-4170

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

Siyuan Qi, Baoxiong Jia, Song-Chun Zhu ; PMLR 80:4171-4179

Do Outliers Ruin Collaboration?

Mingda Qiao ; PMLR 80:4180-4187

Gradually Updated Neural Networks for Large-Scale Image Recognition

Siyuan Qiao, Zhishuai Zhang, Wei Shen, Bo Wang, Alan Yuille ; PMLR 80:4188-4197

DCFNet: Deep Neural Network with Decomposed Convolutional Filters

Qiang Qiu, Xiuyuan Cheng, Calderbank, Guillermo Sapiro ; PMLR 80:4198-4207

Non-convex Conditional Gradient Sliding

Chao Qu, Yan Li, Huan Xu ; PMLR 80:4208-4217

Machine Theory of Mind

Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick ; PMLR 80:4218-4227

Fast Parametric Learning with Activation Memorization

Jack Rae, Chris Dyer, Peter Dayan, Timothy Lillicrap ; PMLR 80:4228-4237

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

Maithra Raghu, Alex Irpan, Jacob Andreas, Bobby Kleinberg, Quoc Le, Jon Kleinberg ; PMLR 80:4238-4246

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

Hugo Raguet, Loic Landrieu ; PMLR 80:4247-4256

Modeling Others using Oneself in Multi-Agent Reinforcement Learning

Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus ; PMLR 80:4257-4266

On Nesting Monte Carlo Estimators

Tom Rainforth, Rob Cornish, Hongseok Yang, Andrew Warrington, Frank Wood ; PMLR 80:4267-4276

Tighter Variational Bounds are Not Necessarily Better

Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh ; PMLR 80:4277-4285

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

Aaditya Ramdas, Tijana Zrnic, Martin Wainwright, Michael Jordan ; PMLR 80:4286-4294

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

Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, Shimon Whiteson ; PMLR 80:4295-4304

Gradient Coding from Cyclic MDS Codes and Expander Graphs

Netanel Raviv, Rashish Tandon, Alex Dimakis, Itzhak Tamo ; PMLR 80:4305-4313

Learning Implicit Generative Models with the Method of Learned Moments

Suman Ravuri, Shakir Mohamed, Mihaela Rosca, Oriol Vinyals ; PMLR 80:4314-4323

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 80:4324-4333

Learning to Reweight Examples for Robust Deep Learning

Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun ; PMLR 80:4334-4343

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 80:4344-4353

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 80:4354-4363

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

Adam Roberts, Jesse Engel, Colin Raffel, Curtis Hawthorne, Douglas Eck ; PMLR 80:4364-4373

Learning to Optimize Combinatorial Functions

Nir Rosenfeld, Eric Balkanski, Amir Globerson, Yaron Singer ; PMLR 80:4374-4383

Fast Information-theoretic Bayesian Optimisation

Binxin Ru, Michael A. Osborne, Mark Mcleod, Diego Granziol ; PMLR 80:4384-4392

Deep One-Class Classification

Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, Marius Kloft ; PMLR 80:4393-4402

Augment and Reduce: Stochastic Inference for Large Categorical Distributions

Francisco Ruiz, Michalis Titsias, Adji Bousso Dieng, David Blei ; PMLR 80:4403-4412

Probabilistic Boolean Tensor Decomposition

Tammo Rukat, Chris Holmes, Christopher Yau ; PMLR 80:4413-4422

Black-Box Variational Inference for Stochastic Differential Equations

Tom Ryder, Andrew Golightly, A. Stephen McGough, Dennis Prangle ; PMLR 80:4423-4432

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

Itay Safran, Ohad Shamir ; PMLR 80:4433-4441

Learning Equations for Extrapolation and Control

Subham Sahoo, Christoph Lampert, Georg Martius ; PMLR 80:4442-4450

Tempered Adversarial Networks

Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf ; PMLR 80:4451-4459

Representation Tradeoffs for Hyperbolic Embeddings

Frederic Sala, Chris De Sa, Albert Gu, Christopher Re ; PMLR 80:4460-4469

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 80:4470-4479

A Classification-Based Study of Covariate Shift in GAN Distributions

Shibani Santurkar, Ludwig Schmidt, Aleksander Madry ; PMLR 80:4480-4489

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

Amartya Sanyal, Matt Kusner, Adria Gascon, Varun Kanade ; PMLR 80:4490-4499

Tight Regret Bounds for Bayesian Optimization in One Dimension

Jonathan Scarlett ; PMLR 80:4500-4508

Learning with Abandonment

Sven Schmit, Ramesh Johari ; PMLR 80:4509-4517

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 80:4518-4527

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 80:4528-4537

Multi-Fidelity Black-Box Optimization with Hierarchical Partitions

Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai ; PMLR 80:4538-4547

Overcoming Catastrophic Forgetting with Hard Attention to the Task

Joan Serra, Didac Suris, Marius Miron, Alexandros Karatzoglou ; PMLR 80:4548-4557

Bounding and Counting Linear Regions of Deep Neural Networks

Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam ; PMLR 80:4558-4566

First Order Generative Adversarial Networks

Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter ; PMLR 80:4567-4576

Finding Influential Training Samples for Gradient Boosted Decision Trees

Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten Rijke ; PMLR 80:4577-4585

Solving Partial Assignment Problems using Random Clique Complexes

Charu Sharma, Deepak Nathani, Manohar Kaul ; PMLR 80:4586-4595

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Noam Shazeer, Mitchell Stern ; PMLR 80:4596-4604

Locally Private Hypothesis Testing

Or Sheffet ; PMLR 80:4605-4614

Learning in Integer Latent Variable Models with Nested Automatic Differentiation

Daniel Sheldon, Kevin Winner, Debora Sujono ; PMLR 80:4615-4623

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

Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian ; PMLR 80:4624-4633

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

Li Shen, Peng Sun, Yitong Wang, Wei Liu, Tong Zhang ; PMLR 80:4634-4643

A Spectral Approach to Gradient Estimation for Implicit Distributions

Jiaxin Shi, Shengyang Sun, Jun Zhu ; PMLR 80:4644-4653

TACO: Learning Task Decomposition via Temporal Alignment for Control

Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, Ingmar Posner ; PMLR 80:4654-4663

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

Wissam Siblini, Pascale Kuntz, Frank Meyer ; PMLR 80:4664-4673

Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization

Umut Simsekli, Cagatay Yildiz, Than Huy Nguyen, Taylan Cemgil, Gael Richard ; PMLR 80:4674-4683

K-means clustering using random matrix sparsification

Kaushik Sinha ; PMLR 80:4684-4692

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 80:4693-4702

An Inference-Based Policy Gradient Method for Learning Options

Matthew Smith, Herke Hoof, Joelle Pineau ; PMLR 80:4703-4712

Accelerating Natural Gradient with Higher-Order Invariance

Yang Song, Jiaming Song, Stefano Ermon ; PMLR 80:4713-4722

Knowledge Transfer with Jacobian Matching

Suraj Srinivas, Francois Fleuret ; PMLR 80:4723-4731

Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control

Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn ; PMLR 80:4732-4741

Structured Control Nets for Deep Reinforcement Learning

Mario Srouji, Jian Zhang, Ruslan Salakhutdinov ; PMLR 80:4742-4751

Approximation Algorithms for Cascading Prediction Models

Matthew Streeter ; PMLR 80:4752-4760

Learning Low-Dimensional Temporal Representations

Bing Su, Ying Wu ; PMLR 80:4761-4770

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

Masanori Suganuma, Mete Ozay, Takayuki Okatani ; PMLR 80:4771-4780

Stagewise Safe Bayesian Optimization with Gaussian Processes

Yanan Sui, Zhuang, Joel Burdick, Yisong Yue ; PMLR 80:4781-4789

Neural Program Synthesis from Diverse Demonstration Videos

Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram, Joseph Lim ; PMLR 80:4790-4799

Scalable approximate Bayesian inference for particle tracking data

Ruoxi Sun, Liam Paninski ; PMLR 80:4800-4809

Graphical Nonconvex Optimization via an Adaptive Convex Relaxation

Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang ; PMLR 80:4810-4817

Convolutional Imputation of Matrix Networks

Qingyun Sun, Mengyuan Yan, David Donoho, boyd ; PMLR 80:4818-4827

Differentiable Compositional Kernel Learning for Gaussian Processes

Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger Grosse ; PMLR 80:4828-4837

Learning the Reward Function for a Misspecified Model

Erik Talvitie ; PMLR 80:4838-4847

$D^2$: Decentralized Training over Decentralized Data

Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu ; PMLR 80:4848-4856

Neural Inverse Rendering for General Reflectance Photometric Stereo

Tatsunori Taniai, Takanori Maehara ; PMLR 80:4857-4866

Black Box FDR

Wesley Tansey, Yixin Wang, David Blei, Raul Rabadan ; PMLR 80:4867-4876

Best Arm Identification in Linear Bandits with Linear Dimension Dependency

Chao Tao, Saúl Blanco, Yuan Zhou ; PMLR 80:4877-4886

Chi-square Generative Adversarial Network

Chenyang Tao, Liqun Chen, Ricardo Henao, Jianfeng Feng, Lawrence Carin Duke ; PMLR 80:4887-4896

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

Adrien Taylor, Bryan Van Scoy, Laurent Lessard ; PMLR 80:4897-4906

Bayesian Uncertainty Estimation for Batch Normalized Deep Networks

Mattias Teye, Hossein Azizpour, Kevin Smith ; PMLR 80:4907-4916

Decoupling Gradient-Like Learning Rules from Representations

Philip Thomas, Christoph Dann, Emma Brunskill ; PMLR 80:4917-4925

CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions

Kevin Tian, Teng Zhang, James Zou ; PMLR 80:4926-4935

Importance Weighted Transfer of Samples in Reinforcement Learning

Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli ; PMLR 80:4936-4945

Adversarial Regression with Multiple Learners

Liang Tong, Sixie Yu, Scott Alfeld, vorobeychik ; PMLR 80:4946-4954

Convergent Tree Backup and Retrace with Function Approximation

Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent ; PMLR 80:4955-4964

Learning Longer-term Dependencies in RNNs with Auxiliary Losses

Trieu Trinh, Andrew Dai, Thang Luong, Quoc Le ; PMLR 80:4965-4974

Theoretical Analysis of Sparse Subspace Clustering with Missing Entries

Manolis Tsakiris, Rene Vidal ; PMLR 80:4975-4984

StrassenNets: Deep Learning with a Multiplication Budget

Michael Tschannen, Aran Khanna, Animashree Anandkumar ; PMLR 80:4985-4994

Invariance of Weight Distributions in Rectified MLPs

Russell Tsuchida, Fred Roosta, Marcus Gallagher ; PMLR 80:4995-5004

Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator

Stephen Tu, Benjamin Recht ; PMLR 80:5005-5014

The Mirage of Action-Dependent Baselines in Reinforcement Learning

George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard Turner, Zoubin Ghahramani, Sergey Levine ; PMLR 80:5015-5024

Adversarial Risk and the Dangers of Evaluating Against Weak Attacks

Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli, Aaron Oord ; PMLR 80:5025-5034

DVAE++: Discrete Variational Autoencoders with Overlapping Transformations

Arash Vahdat, William Macready, Zhengbing Bian, Amir Khoshaman, Evgeny Andriyash ; PMLR 80:5035-5044

Programmatically Interpretable Reinforcement Learning

Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri ; PMLR 80:5045-5054

Clustering Semi-Random Mixtures of Gaussians

Aravindan Vijayaraghavan, Pranjal Awasthi ; PMLR 80:5055-5064

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

Robin Vogel, Aurélien Bellet, Stéphan Clémençon ; PMLR 80:5065-5074

Hierarchical Multi-Label Classification Networks

Jonatas Wehrmann, Ricardo Cerri, Rodrigo Barros ; PMLR 80:5075-5084

Transfer Learning via Learning to Transfer

Ying WEI, Yu Zhang, Junzhou Huang, Qiang Yang ; PMLR 80:5085-5094

Semi-Supervised Learning on Data Streams via Temporal Label Propagation

Tal Wagner, Sudipto Guha, Shiva Kasiviswanathan, Nina Mishra ; PMLR 80:5095-5104

Neural Dynamic Programming for Musical Self Similarity

Christian Walder, Dongwoo Kim ; PMLR 80:5105-5113

Thompson Sampling for Combinatorial Semi-Bandits

Siwei Wang, Wei Chen ; PMLR 80:5114-5122

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 80:5123-5132

Analyzing the Robustness of Nearest Neighbors to Adversarial Examples

Yizhen Wang, Somesh Jha, Kamalika Chaudhuri ; PMLR 80:5133-5142

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

Xingyu Wang, Diego Klabjan ; PMLR 80:5143-5151

Coded Sparse Matrix Multiplication

Sinong Wang, Jiashang Liu, Ness Shroff ; PMLR 80:5152-5160

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 80:5161-5170

Provable Variable Selection for Streaming Features

Jing Wang, Jie Shen, Ping Li ; PMLR 80:5171-5179

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 80:5180-5189

Adversarial Distillation of Bayesian Neural Network Posteriors

Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, Richard Zemel ; PMLR 80:5190-5199

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

Xue Wang, Mingcheng Wei, Tao Yao ; PMLR 80:5200-5208

Online Convolutional Sparse Coding with Sample-Dependent Dictionary

Yaqing Wang, Quanming Yao, James Tin-Yau Kwok, Lionel M. NI ; PMLR 80:5209-5218

Stein Variational Message Passing for Continuous Graphical Models

Dilin Wang, Zhe Zeng, Qiang Liu ; PMLR 80:5219-5227

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

Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni ; PMLR 80:5228-5237

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

Daphna Weinshall, Gad Cohen, Dan Amir ; PMLR 80:5238-5246

Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

Gail Weiss, Yoav Goldberg, Eran Yahav ; PMLR 80:5247-5256

LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration

Gellert Weisz, Andras Gyorgy, Csaba Szepesvari ; PMLR 80:5257-5265

Deep Predictive Coding Network for Object Recognition

Haiguang Wen, Kuan Han, Junxing Shi, Yizhen Zhang, Eugenio Culurciello, Zhongming Liu ; PMLR 80:5266-5275

Towards Fast Computation of Certified Robustness for ReLU Networks

Lily Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane Boning, Inderjit Dhillon ; PMLR 80:5276-5285

Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope

Eric Wong, Zico Kolter ; PMLR 80:5286-5295

Local Density Estimation in High Dimensions

Xian Wu, Moses Charikar, Vishnu Natchu ; PMLR 80:5296-5305

Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits

Huasen Wu, Xueying Guo, Xin Liu ; PMLR 80:5306-5314

SQL-Rank: A Listwise Approach to Collaborative Ranking

Liwei Wu, Cho-Jui Hsieh, James Sharpnack ; PMLR 80:5315-5324

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

Jiaxiang Wu, Weidong Huang, Junzhou Huang, Tong Zhang ; PMLR 80:5325-5333

Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training

Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha ; PMLR 80:5334-5342

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

Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon Du, Stuart Russell ; PMLR 80:5343-5352

Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization

Hang Wu, May Wang ; PMLR 80:5353-5362

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 80:5363-5372

Bayesian Quadrature for Multiple Related Integrals

Xiaoyue Xi, Francois-Xavier Briol, Mark Girolami ; PMLR 80:5373-5382

Model-Level Dual Learning

Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, Tie-Yan Liu ; PMLR 80:5383-5392

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 80:5393-5402

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

Pengtao Xie, Wei Wu, Yichen Zhu, Eric Xing ; PMLR 80:5403-5412

Nonoverlap-Promoting Variable Selection

Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric Xing ; PMLR 80:5413-5422

Learning Semantic Representations for Unsupervised Domain Adaptation

Shaoan Xie, Zibin Zheng, Liang Chen, Chuan Chen ; PMLR 80:5423-5432

Rates of Convergence of Spectral Methods for Graphon Estimation

Jiaming Xu ; PMLR 80:5433-5442

Learning Registered Point Processes from Idiosyncratic Observations

Hongteng Xu, Lawrence Carin, Hongyuan Zha ; PMLR 80:5443-5452

Representation Learning on Graphs with Jumping Knowledge Networks

Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka ; PMLR 80:5453-5462

Learning to Explore via Meta-Policy Gradient

Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng ; PMLR 80:5463-5472

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

Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski ; PMLR 80:5473-5482

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

Ganggang Xu, Zuofeng Shang, Guang Cheng ; PMLR 80:5483-5491

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

Pan Xu, Tianhao Wang, Quanquan Gu ; PMLR 80:5492-5501

A Semantic Loss Function for Deep Learning with Symbolic Knowledge

Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, Guy Broeck ; PMLR 80:5502-5511

Causal Bandits with Propagating Inference

Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi ; PMLR 80:5512-5520

Active Learning with Logged Data

Songbai Yan, Kamalika Chaudhuri, Tara Javidi ; PMLR 80:5521-5530

Binary Classification with Karmic, Threshold-Quasi-Concave Metrics

Bowei Yan, Sanmi Koyejo, Kai Zhong, Pradeep Ravikumar ; PMLR 80:5531-5540

Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions

Karren Yang, Abigail Katcoff, Caroline Uhler ; PMLR 80:5541-5550

Dependent Relational Gamma Process Models for Longitudinal Networks

Sikun Yang, Heinz Koeppl ; PMLR 80:5551-5560

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

Jiasen Yang, Qiang Liu, Vinayak Rao, Jennifer Neville ; PMLR 80:5561-5570

Mean Field Multi-Agent Reinforcement Learning

Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, Jun Wang ; PMLR 80:5571-5580

Yes, but Did It Work?: Evaluating Variational Inference

Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman ; PMLR 80:5581-5590

Hierarchical Text Generation and Planning for Strategic Dialogue

Denis Yarats, Mike Lewis ; PMLR 80:5591-5599

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

Grigory Yaroslavtsev, Adithya Vadapalli ; PMLR 80:5600-5609

Communication-Computation Efficient Gradient Coding

Min Ye, Emmanuel Abbe ; PMLR 80:5610-5619

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

Mao Ye, Yan Sun ; PMLR 80:5620-5629

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 80:5640-5649

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Dong Yin, Yudong Chen, Ramchandran Kannan, Peter Bartlett ; PMLR 80:5650-5659

Semi-Implicit Variational Inference

Mingzhang Yin, Mingyuan Zhou ; PMLR 80:5660-5669

Disentangled Sequential Autoencoder

Li Yingzhen, Stephan Mandt ; PMLR 80:5670-5679

Probably Approximately Metric-Fair Learning

Gal Yona, Guy Rothblum ; PMLR 80:5680-5688

GAIN: Missing Data Imputation using Generative Adversarial Nets

Jinsung Yoon, James Jordon, Mihaela Schaar ; PMLR 80:5689-5698

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

Jinsung Yoon, James Jordon, Mihaela Schaar ; PMLR 80:5699-5707

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, Jure Leskovec ; PMLR 80:5708-5717

An Efficient Semismooth Newton based Algorithm for Convex Clustering

Yancheng Yuan, Defeng Sun, Kim-Chuan Toh ; PMLR 80:5718-5726

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

Alp Yurtsever, Olivier Fercoq, Francesco Locatello, Volkan Cevher ; PMLR 80:5727-5736

Policy Optimization as Wasserstein Gradient Flows

Ruiyi Zhang, Changyou Chen, Chunyuan Li, Lawrence Carin ; PMLR 80:5737-5746

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

Andrea Zanette, Emma Brunskill ; PMLR 80:5747-5755

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

Xiao Zhang, Simon Du, Quanquan Gu ; PMLR 80:5756-5765

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

Richard Zhang, Salar Fattahi, Somayeh Sojoudi ; PMLR 80:5766-5775

High Performance Zero-Memory Overhead Direct Convolutions

Jiyuan Zhang, Franz Franchetti, Tze Meng Low ; PMLR 80:5776-5785

Safe Element Screening for Submodular Function Minimization

Weizhong Zhang, Bin Hong, Lin Ma, Wei Liu, Tong Zhang ; PMLR 80:5786-5795

Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms

Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu ; PMLR 80:5796-5805

Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization

Jiong Zhang, Qi Lei, Inderjit Dhillon ; PMLR 80:5806-5814

Learning Long Term Dependencies via Fourier Recurrent Units

Jiong Zhang, Yibo Lin, Zhao Song, Inderjit Dhillon ; PMLR 80:5815-5823

Tropical Geometry of Deep Neural Networks

Liwen Zhang, Gregory Naitzat, Lek-Heng Lim ; PMLR 80:5824-5832

Deep Bayesian Nonparametric Tracking

Aonan Zhang, John Paisley ; PMLR 80:5833-5841

Composable Planning with Attributes

Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer, Arthur Szlam, Rob Fergus ; PMLR 80:5842-5851

Noisy Natural Gradient as Variational Inference

Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse ; PMLR 80:5852-5861

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

Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu ; PMLR 80:5862-5871

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar ; PMLR 80:5872-5881

Dynamic Regret of Strongly Adaptive Methods

Lijun Zhang, Tianbao Yang, jin, Zhi-Hua Zhou ; PMLR 80:5882-5891

Inter and Intra Topic Structure Learning with Word Embeddings

He Zhao, Lan Du, Wray Buntine, Mingyuan Zhou ; PMLR 80:5892-5901

Adversarially Regularized Autoencoders

Junbo Zhao, Yoon Kim, Kelly Zhang, Alexander Rush, Yann LeCun ; PMLR 80:5902-5911

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 80:5912-5921

Composite Marginal Likelihood Methods for Random Utility Models

Zhibing Zhao, Lirong Xia ; PMLR 80:5922-5931

Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data

Shuai Zheng, James Tin-Yau Kwok ; PMLR 80:5932-5940

A Robust Approach to Sequential Information Theoretic Planning

Sue Zheng, Jason Pacheco, John Fisher ; PMLR 80:5941-5949

Revealing Common Statistical Behaviors in Heterogeneous Populations

Andrey Zhitnikov, Rotem Mulayoff, Tomer Michaeli ; PMLR 80:5950-5959

Understanding Generalization and Optimization Performance of Deep CNNs

Pan Zhou, Jiashi Feng ; PMLR 80:5960-5969

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 80:5970-5979

A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates

Kaiwen Zhou, Fanhua Shang, James Cheng ; PMLR 80:5980-5989

Stochastic Variance-Reduced Cubic Regularized Newton Methods

Dongruo Zhou, Pan Xu, Quanquan Gu ; PMLR 80:5990-5999

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

Yichi Zhou, Jun Zhu, Jingwei Zhuo ; PMLR 80:6000-6008

Distributed Nonparametric Regression under Communication Constraints

Yuancheng Zhu, John Lafferty ; PMLR 80:6009-6017

Message Passing Stein Variational Gradient Descent

Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang ; PMLR 80:6018-6027

Stochastic Variance-Reduced Hamilton Monte Carlo Methods

Difan Zou, Pan Xu, Quanquan Gu ; PMLR 80:6028-6037

Rectify Heterogeneous Models with Semantic Mapping

Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou ; PMLR 80:5630-5639

Hierarchical Long-term Video Prediction without Supervision

Nevan wichers, Ruben Villegas, Dumitru Erhan, Honglak Lee ; PMLR 80:6038-6046

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