ICML 2019

773 papers

Wasserstein of Wasserstein Loss for Learning Generative Models

Yonatan Dukler, Wuchen Li, Alex Lin, Guido Montúfar

Optimal Auctions through Deep Learning

Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David Parkes, Sai Srivatsa Ravindranath

Task-Agnostic Dynamics Priors for Deep Reinforcement Learning

Yilun Du, Karthic Narasimhan

Incorporating Grouping Information into Bayesian Decision Tree Ensembles

Junliang Du, Antonio Linero

Gradient Descent Finds Global Minima of Deep Neural Networks

Simon Du, Jason Lee, Haochuan Li, Liwei Wang, Xiyu Zhai

Provably efficient RL with Rich Observations via Latent State Decoding

Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík

Width Provably Matters in Optimization for Deep Linear Neural Networks

Simon Du, Wei Hu

Generalized No Free Lunch Theorem for Adversarial Robustness

Elvis DOHMATOB

Trajectory-Based Off-Policy Deep Reinforcement Learning

Andreas Doerr, Michael Volpp, Marc Toussaint, Trimpe Sebastian, Christian Daniel

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

Thinh Doan, Siva Maguluri, Justin Romberg

Noisy Dual Principal Component Pursuit

Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, René Vidal

Approximated Oracle Filter Pruning for Destructive CNN Width Optimization

Xiaohan Ding, guiguang ding, Yuchen Guo, Jungong Han, Chenggang Yan

Sever: A Robust Meta-Algorithm for Stochastic Optimization

Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt

Learning to Convolve: A Generalized Weight-Tying Approach

Nichita Diaconu, Daniel Worrall

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

Onur Dereli, Ceyda Oğuz, Mehmet Gönen

Learning-to-Learn Stochastic Gradient Descent with Biased Regularization

Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil

Stochastic Deep Networks

Gwendoline De Bie, Gabriel Peyré, Marco Cuturi

Teaching a black-box learner

Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu

TarMAC: Targeted Multi-Agent Communication

Abhishek Das, Theophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh

A Kernel Theory of Modern Data Augmentation

Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa

Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations

Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Re

Policy Certificates: Towards Accountable Reinforcement Learning

Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill

Bayesian Optimization Meets Bayesian Optimal Stopping

Zhongxiang Dai, Haibin YU, Bryan Kian Hsiang Low, Patrick Jaillet

The Value Function Polytope in Reinforcement Learning

Robert Dadashi, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare

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

Milan Cvitkovic, Badal Singh, Animashree Anandkumar

Minimal Achievable Sufficient Statistic Learning

Milan Cvitkovic, Günther Koliander

Matrix-Free Preconditioning in Online Learning

Ashok Cutkosky, Tamas Sarlos

Anytime Online-to-Batch, Optimism and Acceleration

Ashok Cutkosky

Flexibly Fair Representation Learning by Disentanglement

Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky

Submodular Cost Submodular Cover with an Approximate Oracle

Victoria Crawford, Alan Kuhnle, My Thai

Boosted Density Estimation Remastered

Zac Cranko, Richard Nock

Monge blunts Bayes: Hardness Results for Adversarial Training

Zac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi

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

Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan

Shape Constraints for Set Functions

Andrew Cotter, Maya Gupta, Heinrich Jiang, Erez Louidor, James Muller

Active Learning with Disagreement Graphs

Corinna Cortes, Giulia DeSalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile

Online Learning with Sleeping Experts and Feedback Graphs

Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang

Adjustment Criteria for Generalizing Experimental Findings

Juan Correa, Jin Tian, Elias Bareinboim

Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets

Rob Cornish, Paul Vanetti, Alexandre Bouchard-côté, George Deligiannidis, Arnaud Doucet

A fully differentiable beam search decoder

Ronan Collobert, Awni Hannun, Gabriel Synnaeve

CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning

Cédric Colas, Pierre Fournier, Mohamed CHETOUANI, Olivier Sigaud, Pierre-yves Oudeyer

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling

Certified Adversarial Robustness via Randomized Smoothing

Jeremy Cohen, Elan Rosenfeld, Zico Kolter

Learning Linear-Quadratic Regulators Efficiently with only √T Regret

Alon Cohen, Tomer Koren, Yishay Mansour

Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models

Eldan Cohen, Christopher Beck

Quantifying Generalization in Reinforcement Learning

Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman

On Medians of (Randomized) Pairwise Means

Pierre Laforgue, Stéphan Clémençon, Patrice Bertail

Dimensionality Reduction for Tukey Regression

Kenneth Clarkson, Ruosong Wang, David Woodruff

Sensitivity Analysis of Linear Structural Causal Models

Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim

New results on information theoretic clustering

Ferdinando Cicalese, Eduardo Laber, Lucas Murtinho

Weak Detection of Signal in the Spiked Wigner Model

Hye Won Chung, Ji Oon Lee

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

Eric Chu, Peter Liu

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

Casey Chu, Jose Blanchet, Peter Glynn

Unifying Orthogonal Monte Carlo Methods

Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller

Beyond Backprop: Online Alternating Minimization with Auxiliary Variables

Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti

Neural Joint Source-Channel Coding

Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon

Random Walks on Hypergraphs with Edge-Dependent Vertex Weights

Uthsav Chitra, Benjamin Raphael

Variational Inference for sparse network reconstruction from count data

Julien Chiquet, Stephane Robin, Mahendra Mariadassou

Predictor-Corrector Policy Optimization

Ching-An Cheng, Xinyan Yan, Nathan Ratliff, Byron Boots

Control Regularization for Reduced Variance Reinforcement Learning

Richard Cheng, Abhinav Verma, Gábor Orosz, Swarat Chaudhuri, Yisong Yue

RaFM: Rank-Aware Factorization Machines

Xiaoshuang Chen, Yin Zheng, Jiaxing Wang, Wenye Ma, Junzhou Huang

Robust Decision Trees Against Adversarial Examples

Hongge Chen, Huan Zhang, Duane Boning, Cho-jui Hsieh

Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching

Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan

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

Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang

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

Pin-Yu Chen, Lingfei Wu, Sijia Liu, Indika Rajapakse

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

Xinyang Chen, Sinan Wang, Mingsheng Long, Jianmin Wang

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

Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu

Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

Pengfei Chen, Ben Ben Liao, Guangyong Chen, Shengyu Zhang

Generative Adversarial User Model for Reinforcement Learning Based Recommendation System

Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi

Information-Theoretic Considerations in Batch Reinforcement Learning

Jinglin Chen, Nan Jiang

Proportionally Fair Clustering

Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala

Particle Flow Bayes’ Rule

Xinshi Chen, Hanjun Dai, Le Song

Stein Point Markov Chain Monte Carlo

Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark Girolami

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

George Chen

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

Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan

Neural Network Attributions: A Causal Perspective

Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N Balasubramanian

Online learning with kernel losses

Niladri Chatterji, Aldo Pacchiano, Peter Bartlett

On Symmetric Losses for Learning from Corrupted Labels

Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama

Dynamic Measurement Scheduling for Event Forecasting using Deep RL

Chun-Hao Chang, Mingjie Mai, Anna Goldenberg

Learning Action Representations for Reinforcement Learning

Yash Chandak, Georgios Theocharous, James Kostas, Scott Jordan, Philip Thomas

Automated Model Selection with Bayesian Quadrature

Henry Chai, Jean-Francois Ton, Michael A. Osborne, Roman Garnett

Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games

Adrian Rivera Cardoso, Jacob Abernethy, He Wang, Huan Xu

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

Junyu Cao, Wei Sun

Active Embedding Search via Noisy Paired Comparisons

Gregory Canal, Andy Massimino, Mark Davenport, Christopher Rozell

Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances

Bugra Can, Mert Gurbuzbalaban, Lingjiong Zhu

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

Yongqiang Cai, Qianxiao Li, Zuowei Shen

What is the Effect of Importance Weighting in Deep Learning?

Jonathon Byrd, Zachary Lipton

Rates of Convergence for Sparse Variational Gaussian Process Regression

David Burt, Carl Edward Rasmussen, Mark van der Wilk

Learning Generative Models across Incomparable Spaces

Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka

Self-similar Epochs: Value in arrangement

Eliav Buchnik, Edith Cohen, Avinatan Hasidim, Yossi Matias

Adversarial examples from computational constraints

Sébastien Bubeck, Yin Tat Lee, Eric Price, Ilya Razenshteyn

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

Alon Brutzkus, Amir Globerson

Low Latency Privacy Preserving Inference

Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha

Understanding the Origins of Bias in Word Embeddings

Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel

Deep Counterfactual Regret Minimization

Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations

Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum

Conditioning by adaptive sampling for robust design

David Brookes, Hahnbeom Park, Jennifer Listgarten

Active Manifolds: A non-linear analogue to Active Subspaces

Robert Bridges, Anthony Gruber, Christopher Felder, Miki Verma, Chelsey Hoff

Target Tracking for Contextual Bandits: Application to Demand Side Management

Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz

Coresets for Ordered Weighted Clustering

Vladimir braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu

Blended Conditonal Gradients

Gábor Braun, Sebastian Pokutta, Dan Tu, Stephen Wright