ICML 2019

773 papers

Learning to Clear the Market

Weiran Shen, Sebastien Lahaie, Renato Paes Leme

Conditional Independence in Testing Bayesian Networks

Yujia Shen, Haiying Huang, Arthur Choi, Adnan Darwiche

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

Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant

Exploration Conscious Reinforcement Learning Revisited

Lior Shani, Yonathan Efroni, Shie Mannor

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

Rohin Shah, Noah Gundotra, Pieter Abbeel, Anca Dragan

Discovering Context Effects from Raw Choice Data

Arjun Seshadri, Alex Peysakhovich, Johan Ugander

Weakly-Supervised Temporal Localization via Occurrence Count Learning

Julien Schroeter, Kirill Sidorov, David Marshall

Locally Private Bayesian Inference for Count Models

Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna Wallach

A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar

Breaking Inter-Layer Co-Adaptation by Classifier Anonymization

Ikuro Sato, Kohta Ishikawa, Guoqing Liu, Masayuki Tanaka

Near optimal finite time identification of arbitrary linear dynamical systems

Tuhin Sarkar, Alexander Rakhlin

Multivariate Submodular Optimization

Richard Santiago, F. Bruce Shepherd

Deep Gaussian Processes with Importance-Weighted Variational Inference

Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Deisenroth

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

Touqir Sajed, Or Sheffet

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

Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, Herve Jegou

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

Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang

A Contrastive Divergence for Combining Variational Inference and MCMC

Francisco Ruiz, Michalis Titsias

Statistics and Samples in Distributional Reinforcement Learning

Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare

Iterative Linearized Control: Stable Algorithms and Complexity Guarantees

Vincent Roulet, Siddhartha Srinivasa, Dmitriy Drusvyatskiy, Zaïd Harchaoui

Neuron birth-death dynamics accelerates gradient descent and converges asymptotically

Grant Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden

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

Kevin Roth, Yannic Kilcher, Thomas Hofmann

Good Initializations of Variational Bayes for Deep Models

Simone Rossi, Pietro Michiardi, Maurizio Filippone

Online Convex Optimization in Adversarial Markov Decision Processes

Aviv Rosenberg, Yishay Mansour

Separating value functions across time-scales

Joshua Romoff, Peter Henderson, Ahmed Touati, Emma Brunskill, Joelle Pineau

Efficient learning of smooth probability functions from Bernoulli tests with guarantees

Paul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher

A Persistent Weisfeiler-Lehman Procedure for Graph Classification

Bastian Rieck, Christian Bock, Karsten Borgwardt

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

Alireza Rezaei, Shayan Oveis Gharan

Adversarial Online Learning with noise

Alon Resler, Yishay Mansour

Adaptive Antithetic Sampling for Variance Reduction

Hongyu Ren, Shengjia Zhao, Stefano Ermon

Almost Unsupervised Text to Speech and Automatic Speech Recognition

Yi Ren, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao

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

Henry Reeve, Ata Kaban

Do ImageNet Classifiers Generalize to ImageNet?

Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar

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

Ramin Raziperchikolaei, Harish Bhat

Efficient On-Device Models using Neural Projections

Sujith Ravi

HyperGAN: A Generative Model for Diverse, Performant Neural Networks

Neale Ratzlaff, Li Fuxin

Topological Data Analysis of Decision Boundaries with Application to Model Selection

Karthikeyan Natesan Ramamurthy, Kush Varshney, Krishnan Mody

Screening rules for Lasso with non-convex Sparse Regularizers

Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon

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

Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen

Does Data Augmentation Lead to Positive Margin?

Shashank Rajput, Zhili Feng, Zachary Charles, Po-ling Loh, Dimitris Papailiopoulos

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

Tahrima Rahman, Shasha Jin, Vibhav Gogate

On the Spectral Bias of Neural Networks

Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin

Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function

Arvind Raghunathan, Anoop Cherian, Devesh Jha

Direct Uncertainty Prediction for Medical Second Opinions

Maithra Raghu, Katy Blumer, Rory sayres, Ziad Obermeyer, Bobby Kleinberg

Meta-Learning Neural Bloom Filters

Jack Rae, Sergey Bartunov, Timothy Lillicrap

Learning to Collaborate in Markov Decision Processes

Goran Radanovic, Rati Devidze, David Parkes, Adish Singla

Nonlinear Distributional Gradient Temporal-Difference Learning

Chao Qu, Shie Mannor, Huan Xu

GMNN: Graph Markov Neural Networks

Meng Qu, Yoshua Bengio, Jian Tang

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

Yao Qin, Nicholas Carlini, Garrison Cottrell, Ian Goodfellow, Colin Raffel

Fault Tolerance in Iterative-Convergent Machine Learning

Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric Xing

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

Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Mark Hasegawa-Johnson

SGD: General Analysis and Improved Rates

Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin

SAGA with Arbitrary Sampling

Xun Qian, Zheng Qu, Peter Richtarik

Hiring Under Uncertainty

Manish Purohit, Sreenivas Gollapudi, Manish Raghavan

On Variational Bounds of Mutual Information

Ben Poole, Sherjil Ozair, Aaron van den Oord, Alex Alemi, George Tucker

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

Vladislav Polianskii, Florian T. Pokorny

Temporal Gaussian Mixture Layer for Videos

AJ Piergiovanni, Michael Ryoo

Towards Understanding Knowledge Distillation

Mary Phuong, Christoph Lampert

Cognitive model priors for predicting human decisions

David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths

Exploiting structure of uncertainty for efficient matroid semi-bandits

Pierre Perrault, Vianney Perchet, Michal Valko

Collaborative Channel Pruning for Deep Networks

Hanyu Peng, Jiaxiang Wu, Shifeng Chen, Junzhou Huang

Domain Agnostic Learning with Disentangled Representations

Xingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenko

COMIC: Multi-view Clustering Without Parameter Selection

Xi Peng, Zhenyu Huang, Jiancheng Lv, Hongyuan Zhu, Joey Tianyi Zhou

Fingerprint Policy Optimisation for Robust Reinforcement Learning

Supratik Paul, Michael A. Osborne, Shimon Whiteson

Subspace Robust Wasserstein Distances

François-Pierre Paty, Marco Cuturi

Self-Supervised Exploration via Disagreement

Deepak Pathak, Dhiraj Gandhi, Abhinav Gupta

Spectral Approximate Inference

Sejun Park, Eunho Yang, Se-Young Yun, Jinwoo Shin

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

Daniel Park, Jascha Sohl-dickstein, Quoc Le, Samuel Smith

Variational Laplace Autoencoders

Yookoon Park, Chris Kim, Gunhee Kim

Generalized Majorization-Minimization

Sobhan Naderi Parizi, Kun He, Reza Aghajani, Stan Sclaroff, Pedro Felzenszwalb

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

Vardan Papyan

Deep Residual Output Layers for Neural Language Generation

Nikolaos Pappas, James Henderson

Optimistic Policy Optimization via Multiple Importance Sampling

Matteo Papini, Alberto Maria Metelli, Lorenzo Lupo, Marcello Restelli

Nonparametric Bayesian Deep Networks with Local Competition

Konstantinos Panousis, Sotirios Chatzis, Sergios Theodoridis

Improving Adversarial Robustness via Promoting Ensemble Diversity

Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu

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

Ioannis Panageas, Georgios Piliouras, Xiao Wang

Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?

Samet Oymak, Mahdi Soltanolkotabi

Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding

Muhammad Osama, Dave Zachariah, Thomas B. Schön

Orthogonal Random Forest for Causal Inference

Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu

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

Kenta Oono, Taiji Suzuki

Scalable Learning in Reproducing Kernel Krein Spaces

Dino Oglic, Thomas Gärtner

TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing

Augustus Odena, Catherine Olsson, David Andersen, Ian Goodfellow

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

Peter Ochs, Yura Malitsky

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

Michael Oberst, David Sontag

Tensor Variable Elimination for Plated Factor Graphs

Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin Chiu

Learning to Infer Program Sketches

Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, Armando Solar-Lezama

Remember and Forget for Experience Replay

Guido Novati, Petros Koumoutsakos

Training Neural Networks with Local Error Signals

Arild Nøkland, Lars Hiller Eidnes

Lossless or Quantized Boosting with Integer Arithmetic

Richard Nock, Robert Williamson

Rotation Invariant Householder Parameterization for Bayesian PCA

Rajbir Nirwan, Nils Bertschinger

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

Than Huy Nguyen, Umut Simsekli, Gaël RICHARD

Anomaly Detection With Multiple-Hypotheses Predictions

Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox

On Connected Sublevel Sets in Deep Learning

Quynh Nguyen

Learning to bid in revenue-maximizing auctions

Thomas Nedelec, Noureddine El Karoui, Vianney Perchet

Safe Grid Search with Optimal Complexity

Eugene Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi

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

Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani

A Framework for Bayesian Optimization in Embedded Subspaces

Amin Nayebi, Alexander Munteanu, Matthias Poloczek

Zero-Shot Knowledge Distillation in Deep Networks

Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, Anirban Chakraborty

Learning Context-dependent Label Permutations for Multi-label Classification

Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencía, Sunghyun Park, Ruhi Sarikaya

Hybrid Models with Deep and Invertible Features

Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

Dropout as a Structured Shrinkage Prior

Eric Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth