ICML 2020

1,084 papers

Learning to Simulate Complex Physics with Graph Networks

Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec

Spectral Subsampling MCMC for Stationary Time Series

Robert Salomone, Matias Quiroz, Robert Kohn, Mattias Villani, Minh-Ngoc Tran

A Quantile-based Approach for Hyperparameter Transfer Learning

David Salinas, Huibin Shen, Valerio Perrone

Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization

Sudeep Salgia, Qing Zhao, Sattar Vakili

The Performance Analysis of Generalized Margin Maximizers on Separable Data

Fariborz Salehi, Ehsan Abbasi, Babak Hassibi

Inferring DQN structure for high-dimensional continuous control

Andrey Sakryukin, Chedy Raïssi, Mohan Kankanhalli

Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models

Yuta Saito, Shota Yasui

From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models

Aytunc Sahin, Yatao Bian, Joachim Buhmann, Andreas Krause

Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics

Debjani Saha, Candice Schumann, Duncan McElfresh, John Dickerson, Michelle Mazurek

From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model

Aadirupa Saha, Aditya Gopalan

Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards

Aadirupa Saha, Pierre Gaillard, Michal Valko

An Investigation of Why Overparameterization Exacerbates Spurious Correlations

Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang

Causal Structure Discovery from Distributions Arising from Mixtures of DAGs

Basil Saeed, Snigdha Panigrahi, Caroline Uhler

Radioactive data: tracing through training

Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Herve Jegou

Bounding the fairness and accuracy of classifiers from population statistics

Sivan Sabato, Elad Yom-Tov

Adversarial Attacks on Copyright Detection Systems

Parsa Saadatpanah, Ali Shafahi, Tom Goldstein

Bio-Inspired Hashing for Unsupervised Similarity Search

Chaitanya Ryali, John Hopfield, Leopold Grinberg, Dmitry Krotov

Inter-domain Deep Gaussian Processes

Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal

Bayesian Optimisation over Multiple Continuous and Categorical Inputs

Binxin Ru, Ahsan Alvi, Vu Nguyen, Michael A. Osborne, Stephen Roberts

Simple and sharp analysis of k-means||

Vaclav Rozhon

FetchSGD: Communication-Efficient Federated Learning with Sketching

Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica

Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

Karsten Roth, Timo Milbich, Samarth Sinha, Prateek Gupta, Bjorn Ommer

Certified Robustness to Label-Flipping Attacks via Randomized Smoothing

Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, Zico Kolter

Predicting Choice with Set-Dependent Aggregation

Nir Rosenfeld, Kojin Oshiba, Yaron Singer

Near-optimal Regret Bounds for Stochastic Shortest Path

Aviv Rosenberg, Alon Cohen, Yishay Mansour, Haim Kaplan

Finite-Time Convergence in Continuous-Time Optimization

Orlando Romero, Mouhacine Benosman

Attentive Group Equivariant Convolutional Networks

David Romero, Erik Bekkers, Jakub Tomczak, Mark Hoogendoorn

Reverse-engineering deep ReLU networks

David Rolnick, Konrad Kording

Double-Loop Unadjusted Langevin Algorithm

Paul Rolland, Armin eftekhari, Ali Kavis, Volkan Cevher

Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning

Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Bjorkegren

FR-Train: A Mutual Information-Based Approach to Fair and Robust Training

Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh

On Semi-parametric Inference for BART

Veronika Ročková

Strength from Weakness: Fast Learning Using Weak Supervision

Joshua Robinson, Stefanie Jegelka, Suvrit Sra

Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge

Laura Rieger, Chandan Singh, William Murdoch, Bin Yu

Decentralised Learning with Random Features and Distributed Gradient Descent

Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco

Overfitting in adversarially robust deep learning

Leslie Rice, Eric Wong, Zico Kolter

Normalizing Flows on Tori and Spheres

Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael Albergo, Gurtej Kanwar

NetGAN without GAN: From Random Walks to Low-Rank Approximations

Luca Rendsburg, Holger Heidrich, Ulrike Von Luxburg

The Sample Complexity of Best-k Items Selection from Pairwise Comparisons

Wenbo Ren, Jia Liu, Ness Shroff

Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation

Florence Regol, Soumyasundar Pal, Yingxue Zhang, Mark Coates

Optimistic Bounds for Multi-output Learning

Henry Reeve, Ata Kaban

Learning Human Objectives by Evaluating Hypothetical Behavior

Siddharth Reddy, Anca Dragan, Sergey Levine, Shane Legg, Jan Leike

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

Esteban Real, Chen Liang, David So, Quoc Le

Universal Equivariant Multilayer Perceptrons

Siamak Ravanbakhsh

Implicit Generative Modeling for Efficient Exploration

Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu

Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning

Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla

Closing the convergence gap of SGD without replacement

Shashank Rajput, Anant Gupta, Dimitris Papailiopoulos

A Game Theoretic Framework for Model Based Reinforcement Learning

Aravind Rajeswaran, Igor Mordatch, Vikash Kumar

Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs

Aditya Rajagopal, Diederik Vink, Stylianos Venieris, Christos-Savvas Bouganis

Improving Robustness of Deep-Learning-Based Image Reconstruction

Ankit Raj, Yoram Bresler, Bo Li

Fast Adaptation to New Environments via Policy-Dynamics Value Functions

Roberta Raileanu, Max Goldstein, arthur szlam, Rob Fergus

Understanding and Mitigating the Tradeoff between Robustness and Accuracy

Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, Percy Liang

Transparency Promotion with Model-Agnostic Linear Competitors

Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani

Fast and Private Submodular and k-Submodular Functions Maximization with Matroid Constraints

Akbar Rafiey, Yuichi Yoshida

DeepCoDA: personalized interpretability for compositional health data

Thomas Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

Meng Qu, Tianyu Gao, Louis-Pascal Xhonneux, Jian Tang

Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis

Shuang Qiu, Xiaohan Wei, Zhuoran Yang

Scalable Differentiable Physics for Learning and Control

Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming Lin

Unsupervised Speech Decomposition via Triple Information Bottleneck

Kaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson, David Cox

Deep Isometric Learning for Visual Recognition

Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, Jitendra Malik

Adversarial Risk via Optimal Transport and Optimal Couplings

Muni Sreenivas Pydi, Varun Jog

Graph-based Nearest Neighbor Search: From Practice to Theory

Liudmila Prokhorenkova, Aleksandr Shekhovtsov

SoftSort: A Continuous Relaxation for the argsort Operator

Sebastian Prillo, Julian Eisenschlos

Skew-Fit: State-Covering Self-Supervised Reinforcement Learning

Vitchyr Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl

On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness

Sebastian Pokutta, Mohit Singh, Alfredo Torrico

Explaining Groups of Points in Low-Dimensional Representations

Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar

Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning

Silviu Pitis, Harris Chan, Stephen Zhao, Bradly Stadie, Jimmy Ba

Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation

Konstantinos Pitas

Efficient Domain Generalization via Common-Specific Low-Rank Decomposition

Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi

Randomization matters How to defend against strong adversarial attacks

Rafael Pinot, Raphael Ettedgui, Geovani Rizk, Yann Chevaleyre, Jamal Atif

WaveFlow: A Compact Flow-based Model for Raw Audio

Wei Ping, Kainan Peng, Kexin Zhao, Zhao Song

Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks

Mert Pilanci, Tolga Ergen

Scalable Differential Privacy with Certified Robustness in Adversarial Learning

Hai Phan, My T. Thai, Han Hu, Ruoming Jin, Tong Sun

On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm

Khiem Pham, Khang Le, nhật Hồ, Tung Pham, Hung Bui

IPBoost – Non-Convex Boosting via Integer Programming

Marc Pfetsch, Sebastian Pokutta

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

Aleksei Petrenko, Zhehui Huang, Tushar Kumar, Gaurav Sukhatme, Vladlen Koltun

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion

Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks

Adeel Pervez, Taco Cohen, Efstratios Gavves

Budgeted Online Influence Maximization

Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko

Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks

Dmytro Perekrestenko, Stephan Müller, Helmut Bölcskei

Performative Prediction

Juan Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, Moritz Hardt

Non-Autoregressive Neural Text-to-Speech

Kainan Peng, Wei Ping, Zhao Song, Kexin Zhao

Learning Selection Strategies in Buchberger’s Algorithm

Dylan Peifer, Michael Stillman, Daniel Halpern-Leistner

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina

Acceleration through spectral density estimation

Fabian Pedregosa, Damien Scieur

Reducing Sampling Error in Batch Temporal Difference Learning

Brahma Pavse, Ishan Durugkar, Josiah Hanna, Peter Stone

Regularized Optimal Transport is Ground Cost Adversarial

François-Pierre Paty, Marco Cuturi

Structured Policy Iteration for Linear Quadratic Regulator

Youngsuk Park, Ryan Rossi, Zheng Wen, Gang Wu, Handong Zhao

Meta Variance Transfer: Learning to Augment from the Others

Seong-Jin Park, Seungju Han, Ji-won Baek, Insoo Kim, Juhwan Song

Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis

Jung Yeon Park, Kenneth Carr, Stephan Zheng, Yisong Yue, Rose Yu

Stabilizing Transformers for Reinforcement Learning

Emilio Parisotto, Francis Song, Jack Rae, Razvan Pascanu, Caglar Gulcehre

Adversarial Mutual Information for Text Generation

Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin

Recovery of Sparse Signals from a Mixture of Linear Samples

Soumyabrata Pal, Arya Mazumdar

Neural Clustering Processes

Ari Pakman, Yueqi Wang, Catalin Mitelut, Jinhyung Lee, Liam Paninski

Learning to Score Behaviors for Guided Policy Optimization

Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska

Interferometric Graph Transform: a Deep Unsupervised Graph Representation

Edouard Oyallon

Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?

Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, Daniel Nikovski

On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes

Naoto Ohsaka, Tatsuya Matsuoka

Eliminating the Invariance on the Loss Landscape of Linear Autoencoders

Reza Oftadeh, Jiayi Shen, Zhangyang Wang, Dylan Shell

T-Basis: a Compact Representation for Neural Networks

Anton Obukhov, Maxim Rakhuba, Stamatios Georgoulis, Menelaos Kanakis, Dengxin Dai