Volume 70: International Conference on Machine Learning, 6-11 August 2017, International Convention Centre, Sydney, Australia

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Editors: Doina Precup, Yee Whye Teh

[bib][citeproc]

Uncovering Causality from Multivariate Hawkes Integrated Cumulants

Massil Achab, Emmanuel Bacry, Stéphane Gaı̈ffas, Iacopo Mastromatteo, Jean-François Muzy ; PMLR 70:1-10

A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions

Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh ; PMLR 70:11-21

Constrained Policy Optimization

Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel ; PMLR 70:22-31

The Price of Differential Privacy for Online Learning

Naman Agarwal, Karan Singh ; PMLR 70:32-40

Local Bayesian Optimization of Motor Skills

Riad Akrour, Dmitry Sorokin, Jan Peters, Gerhard Neumann ; PMLR 70:41-50

Connected Subgraph Detection with Mirror Descent on SDPs

Cem Aksoylar, Lorenzo Orecchia, Venkatesh Saligrama ; PMLR 70:51-59

Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis

Ahmed M. Alaa, Scott Hu, Mihaela Schaar ; PMLR 70:60-69

A Semismooth Newton Method for Fast, Generic Convex Programming

Alnur Ali, Eric Wong, J. Zico Kolter ; PMLR 70:70-79

Learning Continuous Semantic Representations of Symbolic Expressions

Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton ; PMLR 70:80-88

Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter

Zeyuan Allen-Zhu ; PMLR 70:89-97

Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition

Zeyuan Allen-Zhu, Yuanzhi Li ; PMLR 70:98-106

Faster Principal Component Regression and Stable Matrix Chebyshev Approximation

Zeyuan Allen-Zhu, Yuanzhi Li ; PMLR 70:107-115

Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU

Zeyuan Allen-Zhu, Yuanzhi Li ; PMLR 70:116-125

Near-Optimal Design of Experiments via Regret Minimization

Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, Yining Wang ; PMLR 70:126-135

OptNet: Differentiable Optimization as a Layer in Neural Networks

Brandon Amos, J. Zico Kolter ; PMLR 70:136-145

Input Convex Neural Networks

Brandon Amos, Lei Xu, J. Zico Kolter ; PMLR 70:146-155

An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation

David Anderson, Ming Gu ; PMLR 70:156-165

Modular Multitask Reinforcement Learning with Policy Sketches

Jacob Andreas, Dan Klein, Sergey Levine ; PMLR 70:166-175

Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning

Oron Anschel, Nir Baram, Nahum Shimkin ; PMLR 70:176-185

A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency

Ron Appel, Pietro Perona ; PMLR 70:186-194

Deep Voice: Real-time Neural Text-to-Speech

Sercan Ö. Arık, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi ; PMLR 70:195-204

Oracle Complexity of Second-Order Methods for Finite-Sum Problems

Yossi Arjevani, Ohad Shamir ; PMLR 70:205-213

Wasserstein Generative Adversarial Networks

Martin Arjovsky, Soumith Chintala, Léon Bottou ; PMLR 70:214-223

Generalization and Equilibrium in Generative Adversarial Nets (GANs)

Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang ; PMLR 70:224-232

A Closer Look at Memorization in Deep Networks

Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, Simon Lacoste-Julien ; PMLR 70:233-242

An Alternative Softmax Operator for Reinforcement Learning

Kavosh Asadi, Michael L. Littman ; PMLR 70:243-252

Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees

Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh ; PMLR 70:253-262

Minimax Regret Bounds for Reinforcement Learning

Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos ; PMLR 70:263-272

Learning the Structure of Generative Models without Labeled Data

Stephen H. Bach, Bryan He, Alexander Ratner, Christopher Ré ; PMLR 70:273-282

Uniform Deviation Bounds for k-Means Clustering

Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause ; PMLR 70:283-291

Distributed and Provably Good Seedings for k-Means in Constant Rounds

Olivier Bachem, Mario Lucic, Andreas Krause ; PMLR 70:292-300

Learning Algorithms for Active Learning

Philip Bachman, Alessandro Sordoni, Adam Trischler ; PMLR 70:301-310

Improving Viterbi is Hard: Better Runtimes Imply Faster Clique Algorithms

Arturs Backurs, Christos Tzamos ; PMLR 70:311-321

Differentially Private Clustering in High-Dimensional Euclidean Spaces

Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang ; PMLR 70:322-331

Strongly-Typed Agents are Guaranteed to Interact Safely

David Balduzzi ; PMLR 70:332-341

The Shattered Gradients Problem: If resnets are the answer, then what is the question?

David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams ; PMLR 70:342-350

Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks

David Balduzzi, Brian McWilliams, Tony Butler-Yeoman ; PMLR 70:351-360

Spectral Learning from a Single Trajectory under Finite-State Policies

Borja Balle, Odalric-Ambrym Maillard ; PMLR 70:361-370

Lost Relatives of the Gumbel Trick

Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller ; PMLR 70:371-379

Dynamic Word Embeddings

Robert Bamler, Stephan Mandt ; PMLR 70:380-389

End-to-End Differentiable Adversarial Imitation Learning

Nir Baram, Oron Anschel, Itai Caspi, Shie Mannor ; PMLR 70:390-399

Emulating the Expert: Inverse Optimization through Online Learning

Andreas Bärmann, Sebastian Pokutta, Oskar Schneider ; PMLR 70:400-410

Unimodal Probability Distributions for Deep Ordinal Classification

Christopher Beckham, Christopher Pal ; PMLR 70:411-419

Globally Induced Forest: A Prepruning Compression Scheme

Jean-Michel Begon, Arnaud Joly, Pierre Geurts ; PMLR 70:420-428

End-to-End Learning for Structured Prediction Energy Networks

David Belanger, Bishan Yang, Andrew McCallum ; PMLR 70:429-439

Learning to Discover Sparse Graphical Models

Eugene Belilovsky, Kyle Kastner, Gael Varoquaux, Matthew B. Blaschko ; PMLR 70:440-448

A Distributional Perspective on Reinforcement Learning

Marc G. Bellemare, Will Dabney, Rémi Munos ; PMLR 70:449-458

Neural Optimizer Search with Reinforcement Learning

Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le ; PMLR 70:459-468

Learning Texture Manifolds with the Periodic Spatial GAN

Urs Bergmann, Nikolay Jetchev, Roland Vollgraf ; PMLR 70:469-477

Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models

Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau ; PMLR 70:478-487

Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret

Alina Beygelzimer, Francesco Orabona, Chicheng Zhang ; PMLR 70:488-497

Guarantees for Greedy Maximization of Non-submodular Functions with Applications

Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek ; PMLR 70:498-507

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

Ilija Bogunovic, Slobodan Mitrović, Jonathan Scarlett, Volkan Cevher ; PMLR 70:508-516

Unsupervised Learning by Predicting Noise

Piotr Bojanowski, Armand Joulin ; PMLR 70:517-526

Adaptive Neural Networks for Efficient Inference

Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama ; PMLR 70:527-536

Compressed Sensing using Generative Models

Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis ; PMLR 70:537-546

Programming with a Differentiable Forth Interpreter

Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel ; PMLR 70:547-556

Practical Gauss-Newton Optimisation for Deep Learning

Aleksandar Botev, Hippolyt Ritter, David Barber ; PMLR 70:557-565

Lazifying Conditional Gradient Algorithms

Gábor Braun, Sebastian Pokutta, Daniel Zink ; PMLR 70:566-575

Clustering High Dimensional Dynamic Data Streams

Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang ; PMLR 70:576-585

On the Sampling Problem for Kernel Quadrature

François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark Girolami ; PMLR 70:586-595

Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning

Noam Brown, Tuomas Sandholm ; PMLR 70:596-604

Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs

Alon Brutzkus, Amir Globerson ; PMLR 70:605-614

Deep Tensor Convolution on Multicores

David Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit ; PMLR 70:615-624

Multi-objective Bandits: Optimizing the Generalized Gini Index

Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Shie Mannor ; PMLR 70:625-634

Priv’IT: Private and Sample Efficient Identity Testing

Bryan Cai, Constantinos Daskalakis, Gautam Kamath ; PMLR 70:635-644

Second-Order Kernel Online Convex Optimization with Adaptive Sketching

Daniele Calandriello, Alessandro Lazaric, Michal Valko ; PMLR 70:645-653

“Convex Until Proven Guilty”: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford ; PMLR 70:654-663

Sliced Wasserstein Kernel for Persistence Diagrams

Mathieu Carrière, Marco Cuturi, Steve Oudot ; PMLR 70:664-673

Multiple Clustering Views from Multiple Uncertain Experts

Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy ; PMLR 70:674-683

Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference

Aditya Chaudhry, Pan Xu, Quanquan Gu ; PMLR 70:684-693

Active Heteroscedastic Regression

Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan ; PMLR 70:694-702

Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning

Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav Sukhatme, Stefan Schaal, Sergey Levine ; PMLR 70:703-711

Robust Structured Estimation with Single-Index Models

Sheng Chen, Arindam Banerjee ; PMLR 70:712-721

Adaptive Multiple-Arm Identification

Jiecao Chen, Xi Chen, Qin Zhang, Yuan Zhou ; PMLR 70:722-730

Dueling Bandits with Weak Regret

Bangrui Chen, Peter I. Frazier ; PMLR 70:731-739

Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions

Yichen Chen, Dongdong Ge, Mengdi Wang, Zizhuo Wang, Yinyu Ye, Hao Yin ; PMLR 70:740-747

Learning to Learn without Gradient Descent by Gradient Descent

Yutian Chen, Matthew W. Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matt Botvinick, Nando Freitas ; PMLR 70:748-756

Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables

Bryant Chen, Daniel Kumor, Elias Bareinboim ; PMLR 70:757-766

Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data

Xixian Chen, Michael R. Lyu, Irwin King ; PMLR 70:767-776

Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability

Zhehui Chen, Lin F. Yang, Chris Junchi Li, Tuo Zhao ; PMLR 70:777-786

Learning to Aggregate Ordinal Labels by Maximizing Separating Width

Guangyong Chen, Shengyu Zhang, Di Lin, Hui Huang, Pheng Ann Heng ; PMLR 70:787-796

Nearly Optimal Robust Matrix Completion

Yeshwanth Cherapanamjeri, Kartik Gupta, Prateek Jain ; PMLR 70:797-805

Algorithms for $\ell_p$ Low-Rank Approximation

Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff ; PMLR 70:806-814

MEC: Memory-efficient Convolution for Deep Neural Network

Minsik Cho, Daniel Brand ; PMLR 70:815-824

On Relaxing Determinism in Arithmetic Circuits

Arthur Choi, Adnan Darwiche ; PMLR 70:825-833

Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution

Po-Wei Chou, Daniel Maturana, Sebastian Scherer ; PMLR 70:834-843

On Kernelized Multi-armed Bandits

Sayak Ray Chowdhury, Aditya Gopalan ; PMLR 70:844-853

Parseval Networks: Improving Robustness to Adversarial Examples

Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, Nicolas Usunier ; PMLR 70:854-863

Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC

Yulai Cong, Bo Chen, Hongwei Liu, Mingyuan Zhou ; PMLR 70:864-873

AdaNet: Adaptive Structural Learning of Artificial Neural Networks

Corinna Cortes, Xavier Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang ; PMLR 70:874-883

Random Feature Expansions for Deep Gaussian Processes

Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone ; PMLR 70:884-893

Soft-DTW: a Differentiable Loss Function for Time-Series

Marco Cuturi, Mathieu Blondel ; PMLR 70:894-903

Understanding Synthetic Gradients and Decoupled Neural Interfaces

Wojciech Marian Czarnecki, Grzegorz Świrszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals, Koray Kavukcuoglu ; PMLR 70:904-912

Stochastic Generative Hashing

Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song ; PMLR 70:913-922

Logarithmic Time One-Against-Some

Hal Daumé III, Nikos Karampatziakis, John Langford, Paul Mineiro ; PMLR 70:923-932

Language Modeling with Gated Convolutional Networks

Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier ; PMLR 70:933-941

An Infinite Hidden Markov Model With Similarity-Biased Transitions

Colin Reimer Dawson, Chaofan Huang, Clayton T. Morrison ; PMLR 70:942-950

Distributed Batch Gaussian Process Optimization

Erik A. Daxberger, Bryan Kian Hsiang Low ; PMLR 70:951-960

Consistency Analysis for Binary Classification Revisited

Krzysztof Dembczyński, Wojciech Kotłowski, Oluwasanmi Koyejo, Nagarajan Natarajan ; PMLR 70:961-969

iSurvive: An Interpretable, Event-time Prediction Model for mHealth

Walter H. Dempsey, Alexander Moreno, Christy K. Scott, Michael L. Dennis, David H. Gustafson, Susan A. Murphy, James M. Rehg ; PMLR 70:970-979

Image-to-Markup Generation with Coarse-to-Fine Attention

Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, Alexander M. Rush ; PMLR 70:980-989

RobustFill: Neural Program Learning under Noisy I/O

Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli ; PMLR 70:990-998

Being Robust (in High Dimensions) Can Be Practical

Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart ; PMLR 70:999-1008

Probabilistic Path Hamiltonian Monte Carlo

Vu Dinh, Arman Bilge, Cheng Zhang, Frederick A. Matsen IV ; PMLR 70:1009-1018

Sharp Minima Can Generalize For Deep Nets

Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio ; PMLR 70:1019-1028

A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI

Justin Domke ; PMLR 70:1029-1038

Dance Dance Convolution

Chris Donahue, Zachary C. Lipton, Julian McAuley ; PMLR 70:1039-1048

Stochastic Variance Reduction Methods for Policy Evaluation

Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou ; PMLR 70:1049-1058

Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement

Jonathan Eckstein, Noam Goldberg, Ai Kagawa ; PMLR 70:1059-1067

Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders

Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Mohammad Norouzi, Douglas Eck, Karen Simonyan ; PMLR 70:1068-1077

Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening

Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso ; PMLR 70:1078-1087

Maximum Selection and Ranking under Noisy Comparisons

Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh ; PMLR 70:1088-1096

Fake News Mitigation via Point Process Based Intervention

Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha ; PMLR 70:1097-1106

Regret Minimization in Behaviorally-Constrained Zero-Sum Games

Gabriele Farina, Christian Kroer, Tuomas Sandholm ; PMLR 70:1107-1116

Coresets for Vector Summarization with Applications to Network Graphs

Dan Feldman, Sedat Ozer, Daniela Rus ; PMLR 70:1117-1125

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Chelsea Finn, Pieter Abbeel, Sergey Levine ; PMLR 70:1126-1135

Input Switched Affine Networks: An RNN Architecture Designed for Interpretability

Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo ; PMLR 70:1136-1145

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H. S. Torr, Pushmeet Kohli, Shimon Whiteson ; PMLR 70:1146-1155

Counterfactual Data-Fusion for Online Reinforcement Learners

Andrew Forney, Judea Pearl, Elias Bareinboim ; PMLR 70:1156-1164

Forward and Reverse Gradient-Based Hyperparameter Optimization

Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil ; PMLR 70:1165-1173

Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier

Joseph Futoma, Sanjay Hariharan, Katherine Heller ; PMLR 70:1174-1182

Deep Bayesian Active Learning with Image Data

Yarin Gal, Riashat Islam, Zoubin Ghahramani ; PMLR 70:1183-1192

Local-to-Global Bayesian Network Structure Learning

Tian Gao, Kshitij Fadnis, Murray Campbell ; PMLR 70:1193-1202

Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis

Dan Garber, Ohad Shamir, Nathan Srebro ; PMLR 70:1203-1212

Differentiable Programs with Neural Libraries

Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow ; PMLR 70:1213-1222

Zonotope Hit-and-run for Efficient Sampling from Projection DPPs

Guillaume Gautier, Rémi Bardenet, Michal Valko ; PMLR 70:1223-1232

No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis

Rong Ge, Chi Jin, Yi Zheng ; PMLR 70:1233-1242

Convolutional Sequence to Sequence Learning

Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin ; PMLR 70:1243-1252

On Context-Dependent Clustering of Bandits

Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Giovanni Zappella, Evans Etrue ; PMLR 70:1253-1262

Neural Message Passing for Quantum Chemistry

Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl ; PMLR 70:1263-1272

Convex Phase Retrieval without Lifting via PhaseMax

Tom Goldstein, Christoph Studer ; PMLR 70:1273-1281

Preferential Bayesian Optimization

Javier González, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence ; PMLR 70:1282-1291

Measuring Sample Quality with Kernels

Jackson Gorham, Lester Mackey ; PMLR 70:1292-1301

Efficient softmax approximation for GPUs

Grave, Armand Joulin, Moustapha Cissé, David Grangier, Hervé Jégou ; PMLR 70:1302-1310

Automated Curriculum Learning for Neural Networks

Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu ; PMLR 70:1311-1320

On Calibration of Modern Neural Networks

Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger ; PMLR 70:1321-1330

ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices

Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain ; PMLR 70:1331-1340

Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs

Michael Gygli, Mohammad Norouzi, Anelia Angelova ; PMLR 70:1341-1351

Reinforcement Learning with Deep Energy-Based Policies

Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine ; PMLR 70:1352-1361

DeepBach: a Steerable Model for Bach Chorales Generation

Gaëtan Hadjeres, François Pachet, Frank Nielsen ; PMLR 70:1362-1371

Consistent On-Line Off-Policy Evaluation

Assaf Hallak, Shie Mannor ; PMLR 70:1372-1383

Faster Greedy MAP Inference for Determinantal Point Processes

Insu Han, Prabhanjan Kambadur, Kyoungsoo Park, Jinwoo Shin ; PMLR 70:1384-1393

Data-Efficient Policy Evaluation Through Behavior Policy Search

Josiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum ; PMLR 70:1394-1403

Joint Dimensionality Reduction and Metric Learning: A Geometric Take

Mehrtash Harandi, Mathieu Salzmann, Richard Hartley ; PMLR 70:1404-1413

Deep IV: A Flexible Approach for Counterfactual Prediction

Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy ; PMLR 70:1414-1423

Robust Guarantees of Stochastic Greedy Algorithms

Avinatan Hassidim, Yaron Singer ; PMLR 70:1424-1432

Efficient Regret Minimization in Non-Convex Games

Elad Hazan, Karan Singh, Cyril Zhang ; PMLR 70:1433-1441

Kernelized Support Tensor Machines

Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin ; PMLR 70:1442-1451

The Sample Complexity of Online One-Class Collaborative Filtering

Reinhard Heckel, Kannan Ramchandran ; PMLR 70:1452-1460

Warped Convolutions: Efficient Invariance to Spatial Transformations

João F. Henriques, Andrea Vedaldi ; PMLR 70:1461-1469

Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space

José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alán Aspuru-Guzik ; PMLR 70:1470-1479

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning

Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner ; PMLR 70:1480-1490

SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling

Jun-ichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe ; PMLR 70:1491-1500

Multilevel Clustering via Wasserstein Means

Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Phung ; PMLR 70:1501-1509

Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo

Matthew D. Hoffman ; PMLR 70:1510-1519

Minimizing Trust Leaks for Robust Sybil Detection

János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz ; PMLR 70:1520-1528

Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks

Mingyi Hong, Davood Hajinezhad, Ming-Min Zhao ; PMLR 70:1529-1538

Analysis and Optimization of Graph Decompositions by Lifted Multicuts

Andrea Horňáková, Jan-Hendrik Lange, Bjoern Andres ; PMLR 70:1539-1548

Dissipativity Theory for Nesterov’s Accelerated Method

Bin Hu, Laurent Lessard ; PMLR 70:1549-1557

Learning Discrete Representations via Information Maximizing Self-Augmented Training

Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama ; PMLR 70:1558-1567

State-Frequency Memory Recurrent Neural Networks

Hao Hu, Guo-Jun Qi ; PMLR 70:1568-1577

Deep Generative Models for Relational Data with Side Information

Changwei Hu, Piyush Rai, Lawrence Carin ; PMLR 70:1578-1586

Toward Controlled Generation of Text

Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing ; PMLR 70:1587-1596

Tensor Decomposition with Smoothness

Masaaki Imaizumi, Kohei Hayashi ; PMLR 70:1597-1606

Variational Inference for Sparse and Undirected Models

John Ingraham, Debora Marks ; PMLR 70:1607-1616

Fairness in Reinforcement Learning

Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth ; PMLR 70:1617-1626

Decoupled Neural Interfaces using Synthetic Gradients

Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu ; PMLR 70:1627-1635

Scalable Generative Models for Multi-label Learning with Missing Labels

Vikas Jain, Nirbhay Modhe, Piyush Rai ; PMLR 70:1636-1644

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control

Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck ; PMLR 70:1645-1654

Bayesian Optimization with Tree-structured Dependencies

Rodolphe Jenatton, Cedric Archambeau, Javier González, Matthias Seeger ; PMLR 70:1655-1664

Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation

Yacine Jernite, Anna Choromanska, David Sontag ; PMLR 70:1665-1674

From Patches to Images: A Nonparametric Generative Model

Geng Ji, Michael C. Hughes, Erik B. Sudderth ; PMLR 70:1675-1683

Density Level Set Estimation on Manifolds with DBSCAN

Heinrich Jiang ; PMLR 70:1684-1693

Uniform Convergence Rates for Kernel Density Estimation

Heinrich Jiang ; PMLR 70:1694-1703

Contextual Decision Processes with low Bellman rank are PAC-Learnable

Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire ; PMLR 70:1704-1713

Efficient Nonmyopic Active Search

Shali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley, Roman Garnett ; PMLR 70:1714-1723

How to Escape Saddle Points Efficiently

Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan ; PMLR 70:1724-1732

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs

Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott Skirlo, Yann LeCun, Max Tegmark, Marin Soljačić ; PMLR 70:1733-1741

An Adaptive Test of Independence with Analytic Kernel Embeddings

Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton ; PMLR 70:1742-1751

StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent

Tyler B. Johnson, Carlos Guestrin ; PMLR 70:1752-1760

Differentially Private Chi-squared Test by Unit Circle Mechanism

Kazuya Kakizaki, Kazuto Fukuchi, Jun Sakuma ; PMLR 70:1761-1770

Video Pixel Networks

Nal Kalchbrenner, Aäron Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu ; PMLR 70:1771-1779

Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP

Satyen Kale, Zohar Karnin, Tengyuan Liang, Dávid Pál ; PMLR 70:1780-1788

Recursive Partitioning for Personalization using Observational Data

Nathan Kallus ; PMLR 70:1789-1798

Multi-fidelity Bayesian Optimisation with Continuous Approximations

Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabás Póczos ; PMLR 70:1799-1808

Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics

Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, Dileep George ; PMLR 70:1809-1818

Learning in POMDPs with Monte Carlo Tree Search

Sammie Katt, Frans A. Oliehoek, Christopher Amato ; PMLR 70:1819-1827

Meritocratic Fairness for Cross-Population Selection

Michael Kearns, Aaron Roth, Zhiwei Steven Wu ; PMLR 70:1828-1836

On Approximation Guarantees for Greedy Low Rank Optimization

Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand Negahban ; PMLR 70:1837-1846

Graph-based Isometry Invariant Representation Learning

Renata Khasanova, Pascal Frossard ; PMLR 70:1847-1856

Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim ; PMLR 70:1857-1865

SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization

Juyong Kim, Yookoon Park, Gunhee Kim, Sung Ju Hwang ; PMLR 70:1866-1874

Cost-Optimal Learning of Causal Graphs

Murat Kocaoglu, Alex Dimakis, Sriram Vishwanath ; PMLR 70:1875-1884

Understanding Black-box Predictions via Influence Functions

Pang Wei Koh, Percy Liang ; PMLR 70:1885-1894

Sub-sampled Cubic Regularization for Non-convex Optimization

Jonas Moritz Kohler, Aurelien Lucchi ; PMLR 70:1895-1904

PixelCNN Models with Auxiliary Variables for Natural Image Modeling

Alexander Kolesnikov, Christoph H. Lampert ; PMLR 70:1905-1914

Active Learning for Cost-Sensitive Classification

Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford ; PMLR 70:1915-1924

Evaluating Bayesian Models with Posterior Dispersion Indices

Alp Kucukelbir, Yixin Wang, David M. Blei ; PMLR 70:1925-1934

Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things

Ashish Kumar, Saurabh Goyal, Manik Varma ; PMLR 70:1935-1944

Grammar Variational Autoencoder

Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato ; PMLR 70:1945-1954

Co-clustering through Optimal Transport

Charlotte Laclau, Ievgen Redko, Basarab Matei, Younès Bennani, Vincent Brault ; PMLR 70:1955-1964

Conditional Accelerated Lazy Stochastic Gradient Descent

Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink ; PMLR 70:1965-1974

Consistent k-Clustering

Silvio Lattanzi, Sergei Vassilvitskii ; PMLR 70:1975-1984

Deep Spectral Clustering Learning

Marc T. Law, Raquel Urtasun, Richard S. Zemel ; PMLR 70:1985-1994

Coordinated Multi-Agent Imitation Learning

Hoang M. Le, Yisong Yue, Peter Carr, Patrick Lucey ; PMLR 70:1995-2003

Bayesian inference on random simple graphs with power law degree distributions

Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi ; PMLR 70:2004-2013

Confident Multiple Choice Learning

Kimin Lee, Changho Hwang, KyoungSoo Park, Jinwoo Shin ; PMLR 70:2014-2023

Deriving Neural Architectures from Sequence and Graph Kernels

Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola ; PMLR 70:2024-2033

Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization

Qi Lei, Ian En-Hsu Yen, Chao-yuan Wu, Inderjit S. Dhillon, Pradeep Ravikumar ; PMLR 70:2034-2042

Learning to Align the Source Code to the Compiled Object Code

Dor Levy, Lior Wolf ; PMLR 70:2043-2051

Dropout Inference in Bayesian Neural Networks with Alpha-divergences

Yingzhen Li, Yarin Gal ; PMLR 70:2052-2061

Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations

Yuanzhi Li, Yingyu Liang ; PMLR 70:2062-2070

Provably Optimal Algorithms for Generalized Linear Contextual Bandits

Lihong Li, Yu Lu, Dengyong Zhou ; PMLR 70:2071-2080

Fast k-Nearest Neighbour Search via Prioritized DCI

Ke Li, Jitendra Malik ; PMLR 70:2081-2090

Forest-type Regression with General Losses and Robust Forest

Alexander Hanbo Li, Andrew Martin ; PMLR 70:2091-2100

Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms

Qianxiao Li, Cheng Tai, Weinan E ; PMLR 70:2101-2110

Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization

Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney ; PMLR 70:2111-2119

Exact MAP Inference by Avoiding Fractional Vertices

Erik M. Lindgren, Alexandros G. Dimakis, Adam Klivans ; PMLR 70:2120-2129

Leveraging Union of Subspace Structure to Improve Constrained Clustering

John Lipor, Laura Balzano ; PMLR 70:2130-2139

Zero-Inflated Exponential Family Embeddings

Li-Ping Liu, David M. Blei ; PMLR 70:2140-2148

Iterative Machine Teaching

Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, Le Song ; PMLR 70:2149-2158

Algorithmic Stability and Hypothesis Complexity

Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao ; PMLR 70:2159-2167

Analogical Inference for Multi-relational Embeddings

Hanxiao Liu, Yuexin Wu, Yiming Yang ; PMLR 70:2168-2178

Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization

Bo Liu, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu, Dimitris N. Metaxas ; PMLR 70:2179-2187

Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling

Hairong Liu, Zhenyao Zhu, Xiangang Li, Sanjeev Satheesh ; PMLR 70:2188-2197

Learning Infinite Layer Networks Without the Kernel Trick

Roi Livni, Daniel Carmon, Amir Globerson ; PMLR 70:2198-2207

Deep Transfer Learning with Joint Adaptation Networks

Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan ; PMLR 70:2208-2217

Multiplicative Normalizing Flows for Variational Bayesian Neural Networks

Christos Louizos, Max Welling ; PMLR 70:2218-2227

How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?

Andreas Loukas ; PMLR 70:2228-2237

Learning Deep Architectures via Generalized Whitened Neural Networks

Ping Luo ; PMLR 70:2238-2246

Learning Gradient Descent: Better Generalization and Longer Horizons

Kaifeng Lv, Shunhua Jiang, Jian Li ; PMLR 70:2247-2255

Spherical Structured Feature Maps for Kernel Approximation

Yueming Lyu ; PMLR 70:2256-2264

Stochastic Gradient MCMC Methods for Hidden Markov Models

Yi-An Ma, Nicholas J. Foti, Emily B. Fox ; PMLR 70:2265-2274

Self-Paced Co-training

Fan Ma, Deyu Meng, Qi Xie, Zina Li, Xuanyi Dong ; PMLR 70:2275-2284

Interactive Learning from Policy-Dependent Human Feedback

James MacGlashan, Mark K. Ho, Robert Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman ; PMLR 70:2285-2294

A Laplacian Framework for Option Discovery in Reinforcement Learning

Marlos C. Machado, Marc G. Bellemare, Michael Bowling ; PMLR 70:2295-2304

Frame-based Data Factorizations

Sebastian Mair, Ahcène Boubekki, Ulf Brefeld ; PMLR 70:2305-2313

Global optimization of Lipschitz functions

Cédric Malherbe, Nicolas Vayatis ; PMLR 70:2314-2323

On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations

Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti ; PMLR 70:2324-2333

Bayesian Models of Data Streams with Hierarchical Power Priors

Andrés Masegosa, Thomas D. Nielsen, Helge Langseth, Darı́o Ramos-López, Antonio Salmerón, Anders L. Madsen ; PMLR 70:2334-2343

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Lucas Maystre, Matthias Grossglauser ; PMLR 70:2344-2353

ChoiceRank: Identifying Preferences from Node Traffic in Networks

Lucas Maystre, Matthias Grossglauser ; PMLR 70:2354-2362

Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

Mason McGill, Pietro Perona ; PMLR 70:2363-2372

Risk Bounds for Transferring Representations With and Without Fine-Tuning

Daniel McNamara, Maria-Florina Balcan ; PMLR 70:2373-2381

Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates

Jiali Mei, Yohann De Castro, Yannig Goude, Georges Hébrail ; PMLR 70:2382-2390

Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks

Lars Mescheder, Sebastian Nowozin, Andreas Geiger ; PMLR 70:2391-2400

Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections

Zakaria Mhammedi, Andrew Hellicar, Ashfaqur Rahman, James Bailey ; PMLR 70:2401-2409

Discovering Discrete Latent Topics with Neural Variational Inference

Yishu Miao, Edward Grefenstette, Phil Blunsom ; PMLR 70:2410-2419

Variational Boosting: Iteratively Refining Posterior Approximations

Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams ; PMLR 70:2420-2429

Device Placement Optimization with Reinforcement Learning

Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean ; PMLR 70:2430-2439

Tight Bounds for Approximate Carathéodory and Beyond

Vahab Mirrokni, Renato Paes Leme, Adrian Vladu, Sam Chiu-wai Wong ; PMLR 70:2440-2448

Deletion-Robust Submodular Maximization: Data Summarization with “the Right to be Forgotten”

Baharan Mirzasoleiman, Amin Karbasi, Andreas Krause ; PMLR 70:2449-2458

Prediction and Control with Temporal Segment Models

Nikhil Mishra, Pieter Abbeel, Igor Mordatch ; PMLR 70:2459-2468

Improving Gibbs Sampler Scan Quality with DoGS

Ioannis Mitliagkas, Lester Mackey ; PMLR 70:2469-2477

Differentially Private Submodular Maximization: Data Summarization in Disguise

Marko Mitrovic, Mark Bun, Andreas Krause, Amin Karbasi ; PMLR 70:2478-2487

Active Learning for Top-$K$ Rank Aggregation from Noisy Comparisons

Soheil Mohajer, Changho Suh, Adel Elmahdy ; PMLR 70:2488-2497

Variational Dropout Sparsifies Deep Neural Networks

Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov ; PMLR 70:2498-2507

Regularising Non-linear Models Using Feature Side-information

Amina Mollaysa, Pablo Strasser, Alexandros Kalousis ; PMLR 70:2508-2517

Coupling Distributed and Symbolic Execution for Natural Language Queries

Lili Mou, Zhengdong Lu, Hang Li, Zhi Jin ; PMLR 70:2518-2526

McGan: Mean and Covariance Feature Matching GAN

Youssef Mroueh, Tom Sercu, Vaibhava Goel ; PMLR 70:2527-2535

Sequence to Better Sequence: Continuous Revision of Combinatorial Structures

Jonas Mueller, David Gifford, Tommi Jaakkola ; PMLR 70:2536-2544

Variants of RMSProp and Adagrad with Logarithmic Regret Bounds

Mahesh Chandra Mukkamala, Matthias Hein ; PMLR 70:2545-2553

Meta Networks

Tsendsuren Munkhdalai, Hong Yu ; PMLR 70:2554-2563

Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition

Tasha Nagamine, Nima Mesgarani ; PMLR 70:2564-2573

Adaptive Sampling Probabilities for Non-Smooth Optimization

Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi ; PMLR 70:2574-2583

Delta Networks for Optimized Recurrent Network Computation

Daniel Neil, Jun Haeng Lee, Tobi Delbruck, Shih-Chii Liu ; PMLR 70:2584-2593

Post-Inference Prior Swapping

Willie Neiswanger, Eric Xing ; PMLR 70:2594-2602

The Loss Surface of Deep and Wide Neural Networks

Quynh Nguyen, Matthias Hein ; PMLR 70:2603-2612

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takáč ; PMLR 70:2613-2621

Composing Tree Graphical Models with Persistent Homology Features for Clustering Mixed-Type Data

Xiuyan Ni, Novi Quadrianto, Yusu Wang, Chao Chen ; PMLR 70:2622-2631

Multichannel End-to-end Speech Recognition

Tsubasa Ochiai, Shinji Watanabe, Takaaki Hori, John R. Hershey ; PMLR 70:2632-2641

Conditional Image Synthesis with Auxiliary Classifier GANs

Augustus Odena, Christopher Olah, Jonathon Shlens ; PMLR 70:2642-2651

Nyström Method with Kernel K-means++ Samples as Landmarks

Dino Oglic, Thomas Gärtner ; PMLR 70:2652-2660

Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning

Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli ; PMLR 70:2661-2670

The Statistical Recurrent Unit

Junier B. Oliva, Barnabás Póczos, Jeff Schneider ; PMLR 70:2671-2680

Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability

Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian ; PMLR 70:2681-2690

Algebraic Variety Models for High-Rank Matrix Completion

Greg Ongie, Rebecca Willett, Robert D. Nowak, Laura Balzano ; PMLR 70:2691-2700

Why is Posterior Sampling Better than Optimism for Reinforcement Learning?

Ian Osband, Benjamin Van Roy ; PMLR 70:2701-2710

Bidirectional Learning for Time-series Models with Hidden Units

Takayuki Osogami, Hiroshi Kajino, Taro Sekiyama ; PMLR 70:2711-2720

Count-Based Exploration with Neural Density Models

Georg Ostrovski, Marc G. Bellemare, Aäron Oord, Rémi Munos ; PMLR 70:2721-2730

Dictionary Learning Based on Sparse Distribution Tomography

Pedram Pad, Farnood Salehi, Elisa Celis, Patrick Thiran, Michael Unser ; PMLR 70:2731-2740

Stochastic Bouncy Particle Sampler

Ari Pakman, Dar Gilboa, David Carlson, Liam Paninski ; PMLR 70:2741-2750

A Birth-Death Process for Feature Allocation

Konstantina Palla, David Knowles, Zoubin Ghahramani ; PMLR 70:2751-2759

Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control

Yunpeng Pan, Xinyan Yan, Evangelos A. Theodorou, Byron Boots ; PMLR 70:2760-2768

Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery

Ashkan Panahi, Devdatt Dubhashi, Fredrik D. Johansson, Chiranjib Bhattacharyya ; PMLR 70:2769-2777

Curiosity-driven Exploration by Self-supervised Prediction

Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell ; PMLR 70:2778-2787

Asynchronous Distributed Variational Gaussian Process for Regression

Hao Peng, Shandian Zhe, Xiao Zhang, Yuan Qi ; PMLR 70:2788-2797

Geometry of Neural Network Loss Surfaces via Random Matrix Theory

Jeffrey Pennington, Yasaman Bahri ; PMLR 70:2798-2806

Multi-task Learning with Labeled and Unlabeled Tasks

Anastasia Pentina, Christoph H. Lampert ; PMLR 70:2807-2816

Robust Adversarial Reinforcement Learning

Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta ; PMLR 70:2817-2826

Neural Episodic Control

Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell ; PMLR 70:2827-2836

Online and Linear-Time Attention by Enforcing Monotonic Alignments

Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck ; PMLR 70:2837-2846

On the Expressive Power of Deep Neural Networks

Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein ; PMLR 70:2847-2854

Estimating the unseen from multiple populations

Aditi Raghunathan, Gregory Valiant, James Zou ; PMLR 70:2855-2863

Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery

Mostafa Rahmani, George Atia ; PMLR 70:2864-2873

Innovation Pursuit: A New Approach to the Subspace Clustering Problem

Mostafa Rahmani, George Atia ; PMLR 70:2874-2882

High Dimensional Bayesian Optimization with Elastic Gaussian Process

Santu Rana, Cheng Li, Sunil Gupta, Vu Nguyen, Svetha Venkatesh ; PMLR 70:2883-2891

Equivariance Through Parameter-Sharing

Siamak Ravanbakhsh, Jeff Schneider, Barnabás Póczos ; PMLR 70:2892-2901

Large-Scale Evolution of Image Classifiers

Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V. Le, Alexey Kurakin ; PMLR 70:2902-2911

Parallel Multiscale Autoregressive Density Estimation

Scott Reed, Aäron Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, Nando Freitas ; PMLR 70:2912-2921

Real-Time Adaptive Image Compression

Oren Rippel, Lubomir Bourdev ; PMLR 70:2922-2930

Active Learning for Accurate Estimation of Linear Models

Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric ; PMLR 70:2931-2939

Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study

Samuel Ritter, David G. T. Barrett, Adam Santoro, Matt M. Botvinick ; PMLR 70:2940-2949

Pain-Free Random Differential Privacy with Sensitivity Sampling

Benjamin I. P. Rubinstein, Francesco Aldà ; PMLR 70:2950-2959

Enumerating Distinct Decision Trees

Salvatore Ruggieri ; PMLR 70:2960-2968

Bayesian Boolean Matrix Factorisation

Tammo Rukat, Chris C. Holmes, Michalis K. Titsias, Christopher Yau ; PMLR 70:2969-2978

Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks

Itay Safran, Ohad Shamir ; PMLR 70:2979-2987

Asymmetric Tri-training for Unsupervised Domain Adaptation

Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada ; PMLR 70:2988-2997

Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data

Tomoya Sakai, Marthinus Christoffel Plessis, Gang Niu, Masashi Sugiyama ; PMLR 70:2998-3006

Analytical Guarantees on Numerical Precision of Deep Neural Networks

Charbel Sakr, Yongjune Kim, Naresh Shanbhag ; PMLR 70:3007-3016

Hierarchy Through Composition with Multitask LMDPs

Andrew M. Saxe, Adam C. Earle, Benjamin Rosman ; PMLR 70:3017-3026

Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks

Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié ; PMLR 70:3027-3036

Adapting Kernel Representations Online Using Submodular Maximization

Matthew Schlegel, Yangchen Pan, Jiecao Chen, Martha White ; PMLR 70:3037-3046

Developing Bug-Free Machine Learning Systems With Formal Mathematics

Daniel Selsam, Percy Liang, David L. Dill ; PMLR 70:3047-3056

Identifying Best Interventions through Online Importance Sampling

Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai ; PMLR 70:3057-3066

Failures of Gradient-Based Deep Learning

Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah ; PMLR 70:3067-3075

Estimating individual treatment effect: generalization bounds and algorithms

Uri Shalit, Fredrik D. Johansson, David Sontag ; PMLR 70:3076-3085

Online Learning with Local Permutations and Delayed Feedback

Ohad Shamir, Liran Szlak ; PMLR 70:3086-3094

Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use

Vatsal Sharan, Gregory Valiant ; PMLR 70:3095-3104

Differentially Private Ordinary Least Squares

Or Sheffet ; PMLR 70:3105-3114

On the Iteration Complexity of Support Recovery via Hard Thresholding Pursuit

Jie Shen, Ping Li ; PMLR 70:3115-3124

GSOS: Gauss-Seidel Operator Splitting Algorithm for Multi-Term Nonsmooth Convex Composite Optimization

Li Shen, Wei Liu, Ganzhao Yuan, Shiqian Ma ; PMLR 70:3125-3134

World of Bits: An Open-Domain Platform for Web-Based Agents

Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, Percy Liang ; PMLR 70:3135-3144

Learning Important Features Through Propagating Activation Differences

Avanti Shrikumar, Peyton Greenside, Anshul Kundaje ; PMLR 70:3145-3153

Optimal Densification for Fast and Accurate Minwise Hashing

Anshumali Shrivastava ; PMLR 70:3154-3163

Bottleneck Conditional Density Estimation

Rui Shu, Hung H. Bui, Mohammad Ghavamzadeh ; PMLR 70:3164-3172

Attentive Recurrent Comparators

Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati ; PMLR 70:3173-3181

Gradient Boosted Decision Trees for High Dimensional Sparse Output

Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh ; PMLR 70:3182-3190

The Predictron: End-To-End Learning and Planning

David Silver, Hado Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris ; PMLR 70:3191-3199

Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo

Umut Şimşekli ; PMLR 70:3200-3209

Nonparanormal Information Estimation

Shashank Singh, Barnabás Póczos ; PMLR 70:3210-3219

High-Dimensional Structured Quantile Regression

Vidyashankar Sivakumar, Arindam Banerjee ; PMLR 70:3220-3229

Robust Budget Allocation via Continuous Submodular Functions

Matthew Staib, Stefanie Jegelka ; PMLR 70:3230-3240

Probabilistic Submodular Maximization in Sub-Linear Time

Serban Stan, Morteza Zadimoghaddam, Andreas Krause, Amin Karbasi ; PMLR 70:3241-3250

Approximate Steepest Coordinate Descent

Sebastian U. Stich, Anant Raj, Martin Jaggi ; PMLR 70:3251-3259

Ordinal Graphical Models: A Tale of Two Approaches

Arun Sai Suggala, Eunho Yang, Pradeep Ravikumar ; PMLR 70:3260-3269

Tensor Balancing on Statistical Manifold

Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda ; PMLR 70:3270-3279

Safety-Aware Algorithms for Adversarial Contextual Bandit

Wen Sun, Debadeepta Dey, Ashish Kapoor ; PMLR 70:3280-3288

Relative Fisher Information and Natural Gradient for Learning Large Modular Models

Ke Sun, Frank Nielsen ; PMLR 70:3289-3298

meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting

Xu Sun, Xuancheng Ren, Shuming Ma, Houfeng Wang ; PMLR 70:3299-3308

Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction

Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell ; PMLR 70:3309-3318

Axiomatic Attribution for Deep Networks

Mukund Sundararajan, Ankur Taly, Qiqi Yan ; PMLR 70:3319-3328

Distributed Mean Estimation with Limited Communication

Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan ; PMLR 70:3329-3337

Selective Inference for Sparse High-Order Interaction Models

Shinya Suzumura, Kazuya Nakagawa, Yuta Umezu, Koji Tsuda, Ichiro Takeuchi ; PMLR 70:3338-3347

Coherent Probabilistic Forecasts for Hierarchical Time Series

Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman ; PMLR 70:3348-3357

Partitioned Tensor Factorizations for Learning Mixed Membership Models

Zilong Tan, Sayan Mukherjee ; PMLR 70:3358-3367

Gradient Coding: Avoiding Stragglers in Distributed Learning

Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis ; PMLR 70:3368-3376

Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares

Junqi Tang, Mohammad Golbabaee, Mike E. Davies ; PMLR 70:3377-3386

Neural Networks and Rational Functions

Matus Telgarsky ; PMLR 70:3387-3393

Stochastic DCA for the Large-sum of Non-convex Functions Problem and its Application to Group Variable Selection in Classification

Hoai An Le Thi, Hoai Minh Le, Duy Nhat Phan, Bach Tran ; PMLR 70:3394-3403

An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis

Yuandong Tian ; PMLR 70:3404-3413

Evaluating the Variance of Likelihood-Ratio Gradient Estimators

Seiya Tokui, Issei Sato ; PMLR 70:3414-3423

Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin ; PMLR 70:3424-3433

Boosted Fitted Q-Iteration

Samuele Tosatto, Matteo Pirotta, Carlo D’Eramo, Marcello Restelli ; PMLR 70:3434-3443

Diameter-Based Active Learning

Christopher Tosh, Sanjoy Dasgupta ; PMLR 70:3444-3452

Magnetic Hamiltonian Monte Carlo

Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner ; PMLR 70:3453-3461

Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song ; PMLR 70:3462-3471

Hyperplane Clustering via Dual Principal Component Pursuit

Manolis C. Tsakiris, René Vidal ; PMLR 70:3472-3481

Breaking Locality Accelerates Block Gauss-Seidel

Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht ; PMLR 70:3482-3491

Multilabel Classification with Group Testing and Codes

Shashanka Ubaru, Arya Mazumdar ; PMLR 70:3492-3501

Learning Stable Stochastic Nonlinear Dynamical Systems

Jonas Umlauft, Sandra Hirche ; PMLR 70:3502-3510

Learning Determinantal Point Processes with Moments and Cycles

John Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet ; PMLR 70:3511-3520

Automatic Discovery of the Statistical Types of Variables in a Dataset

Isabel Valera, Zoubin Ghahramani ; PMLR 70:3521-3529

Model-Independent Online Learning for Influence Maximization

Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt ; PMLR 70:3530-3539

FeUdal Networks for Hierarchical Reinforcement Learning

Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu ; PMLR 70:3540-3549

Scalable Multi-Class Gaussian Process Classification using Expectation Propagation

Carlos Villacampa-Calvo, Daniel Hernández-Lobato ; PMLR 70:3550-3559

Learning to Generate Long-term Future via Hierarchical Prediction

Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee ; PMLR 70:3560-3569

On orthogonality and learning recurrent networks with long term dependencies

Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal ; PMLR 70:3570-3578

Fast Bayesian Intensity Estimation for the Permanental Process

Christian J. Walder, Adrian N. Bishop ; PMLR 70:3579-3588

Optimal and Adaptive Off-policy Evaluation in Contextual Bandits

Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudı́k ; PMLR 70:3589-3597

Capacity Releasing Diffusion for Speed and Locality

Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao ; PMLR 70:3598-3607

Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging

Shusen Wang, Alex Gittens, Michael W. Mahoney ; PMLR 70:3608-3616

Robust Gaussian Graphical Model Estimation with Arbitrary Corruption

Lingxiao Wang, Quanquan Gu ; PMLR 70:3617-3626

Max-value Entropy Search for Efficient Bayesian Optimization

Zi Wang, Stefanie Jegelka ; PMLR 70:3627-3635

Efficient Distributed Learning with Sparsity

Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang ; PMLR 70:3636-3645

Robust Probabilistic Modeling with Bayesian Data Reweighting

Yixin Wang, Alp Kucukelbir, David M. Blei ; PMLR 70:3646-3655

Batched High-dimensional Bayesian Optimization via Structural Kernel Learning

Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli ; PMLR 70:3656-3664

Tensor Decomposition via Simultaneous Power Iteration

Po-An Wang, Chi-Jen Lu ; PMLR 70:3665-3673

Sequence Modeling via Segmentations

Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou, Li Deng ; PMLR 70:3674-3683

Variational Policy for Guiding Point Processes

Yichen Wang, Grady Williams, Evangelos Theodorou, Le Song ; PMLR 70:3684-3693

Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms

Jialei Wang, Lin Xiao ; PMLR 70:3694-3702

Beyond Filters: Compact Feature Map for Portable Deep Model

Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao ; PMLR 70:3703-3711

A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery

Lingxiao Wang, Xiao Zhang, Quanquan Gu ; PMLR 70:3712-3721

Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression

Pengfei Wei, Ramon Sagarna, Yiping Ke, Yew-Soon Ong, Chi-Keong Goh ; PMLR 70:3722-3731

Latent Intention Dialogue Models

Tsung-Hsien Wen, Yishu Miao, Phil Blunsom, Steve Young ; PMLR 70:3732-3741

Unifying Task Specification in Reinforcement Learning

Martha White ; PMLR 70:3742-3750

Learned Optimizers that Scale and Generalize

Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Nando Freitas, Jascha Sohl-Dickstein ; PMLR 70:3751-3760

Exact Inference for Integer Latent-Variable Models

Kevin Winner, Debora Sujono, Dan Sheldon ; PMLR 70:3761-3770

Tensor Belief Propagation

Andrew Wrigley, Wee Sun Lee, Nan Ye ; PMLR 70:3771-3779

A Unified View of Multi-Label Performance Measures

Xi-Zhu Wu, Zhi-Hua Zhou ; PMLR 70:3780-3788

Dual Supervised Learning

Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu ; PMLR 70:3789-3798

Learning Latent Space Models with Angular Constraints

Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing ; PMLR 70:3799-3810

Uncorrelation and Evenness: a New Diversity-Promoting Regularizer

Pengtao Xie, Aarti Singh, Eric P. Xing ; PMLR 70:3811-3820

Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence

Yi Xu, Qihang Lin, Tianbao Yang ; PMLR 70:3821-3830

Learning Hawkes Processes from Short Doubly-Censored Event Sequences

Hongteng Xu, Dixin Luo, Hongyuan Zha ; PMLR 70:3831-3840

Adaptive Consensus ADMM for Distributed Optimization

Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein ; PMLR 70:3841-3850

High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation

Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu ; PMLR 70:3851-3860

Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering

Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong ; PMLR 70:3861-3870

On The Projection Operator to A Three-view Cardinality Constrained Set

Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, Ji Liu ; PMLR 70:3871-3880

Improved Variational Autoencoders for Text Modeling using Dilated Convolutions

Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, Taylor Berg-Kirkpatrick ; PMLR 70:3881-3890

Tensor-Train Recurrent Neural Networks for Video Classification

Yinchong Yang, Denis Krompass, Volker Tresp ; PMLR 70:3891-3900

A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates

Tianbao Yang, Qihang Lin, Lijun Zhang ; PMLR 70:3901-3910

Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity

Eunho Yang, Aurélie C. Lozano ; PMLR 70:3911-3920

Scalable Bayesian Rule Lists

Hongyu Yang, Cynthia Rudin, Margo Seltzer ; PMLR 70:3921-3930

Approximate Newton Methods and Their Local Convergence

Haishan Ye, Luo Luo, Zhihua Zhang ; PMLR 70:3931-3939

A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization

Jianbo Ye, James Z. Wang, Jia Li ; PMLR 70:3940-3948

Latent Feature Lasso

Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar ; PMLR 70:3949-3957

Combined Group and Exclusive Sparsity for Deep Neural Networks

Jaehong Yoon, Sung Ju Hwang ; PMLR 70:3958-3966

Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data

Manzil Zaheer, Amr Ahmed, Alexander J. Smola ; PMLR 70:3967-3976

Canopy Fast Sampling with Cover Trees

Manzil Zaheer, Satwik Kottur, Amr Ahmed, José Moura, Alex Smola ; PMLR 70:3977-3986

Continual Learning Through Synaptic Intelligence

Friedemann Zenke, Ben Poole, Surya Ganguli ; PMLR 70:3987-3995

Stochastic Gradient Monomial Gamma Sampler

Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin ; PMLR 70:3996-4005

Adversarial Feature Matching for Text Generation

Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, Lawrence Carin ; PMLR 70:4006-4015

Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction

Weizhong Zhang, Bin Hong, Wei Liu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang ; PMLR 70:4016-4025

Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method

Chenzi Zhang, Shuguang Hu, Zhihao Gavin Tang, T-H. Hubert Chan ; PMLR 70:4026-4034

ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning

Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang ; PMLR 70:4035-4043

Convexified Convolutional Neural Networks

Yuchen Zhang, Percy Liang, Martin J. Wainwright ; PMLR 70:4044-4053

Projection-free Distributed Online Learning in Networks

Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang ; PMLR 70:4054-4062

Multi-Class Optimal Margin Distribution Machine

Teng Zhang, Zhi-Hua Zhou ; PMLR 70:4063-4071

Leveraging Node Attributes for Incomplete Relational Data

He Zhao, Lan Du, Wray Buntine ; PMLR 70:4072-4081

Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang, Bo Yuan ; PMLR 70:4082-4090

Learning Hierarchical Features from Deep Generative Models

Shengjia Zhao, Jiaming Song, Stefano Ermon ; PMLR 70:4091-4099

Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture

Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi ; PMLR 70:4100-4109

Follow the Moving Leader in Deep Learning

Shuai Zheng, James T. Kwok ; PMLR 70:4110-4119

Asynchronous Stochastic Gradient Descent with Delay Compensation

Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhi-Ming Ma, Tie-Yan Liu ; PMLR 70:4120-4129

Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible

Kai Zheng, Wenlong Mou, Liwei Wang ; PMLR 70:4130-4139

Recovery Guarantees for One-hidden-layer Neural Networks

Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, Inderjit S. Dhillon ; PMLR 70:4140-4149

Stochastic Adaptive Quasi-Newton Methods for Minimizing Expected Values

Chaoxu Zhou, Wenbo Gao, Donald Goldfarb ; PMLR 70:4150-4159

Identify the Nash Equilibrium in Static Games with Random Payoffs

Yichi Zhou, Jialian Li, Jun Zhu ; PMLR 70:4160-4169

When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications

Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba, Vikas Singh ; PMLR 70:4170-4179

High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm

Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu ; PMLR 70:4180-4188

Recurrent Highway Networks

Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutnı́k, Jürgen Schmidhuber ; PMLR 70:4189-4198

Online Learning to Rank in Stochastic Click Models

Masrour Zoghi, Tomas Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvari, Zheng Wen ; PMLR 70:4199-4208

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