ICML 2020

1,084 papers

Alleviating Privacy Attacks via Causal Learning

Shruti Tople, Amit Sharma, Aditya Nori

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

Alexander Tong, Jessie Huang, Guy Wolf, David van Dijk, Smita Krishnaswamy

Choice Set Optimization Under Discrete Choice Models of Group Decisions

Kiran Tomlinson, Austin Benson

Multi-step Greedy Reinforcement Learning Algorithms

Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh

Convolutional dictionary learning based auto-encoders for natural exponential-family distributions

Bahareh Tolooshams, Andrew Song, Simona Temereanca, Demba Ba

Sequential Transfer in Reinforcement Learning with a Generative Model

Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli

Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension

Yuandong Tian

Few-shot Domain Adaptation by Causal Mechanism Transfer

Takeshi Teshima, Issei Sato, Masashi Sugiyama

Inductive Relation Prediction by Subgraph Reasoning

Komal Teru, Etienne Denis, Will Hamilton

Sparse Sinkhorn Attention

Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, Da-Cheng Juan

No-Regret Exploration in Goal-Oriented Reinforcement Learning

Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric

Learning disconnected manifolds: a no GAN’s land

Ugo Tanielian, Thibaut Issenhuth, Elvis DOHMATOB, Jeremie Mary

Variational Imitation Learning with Diverse-quality Demonstrations

Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama

Taylor Expansion Policy Optimization

Yunhao Tang, Michal Valko, Rémi Munos

Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies

Shengpu Tang, Aditya Modi, Michael Sjoding, Jenna Wiens

The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application

Wenpin Tang, Xin Guo, Fengmin Tang

Reinforcement Learning for Integer Programming: Learning to Cut

Yunhao Tang, Shipra Agrawal, Yuri Faenza

DropNet: Reducing Neural Network Complexity via Iterative Pruning

Chong Min John Tan, Mehul Motani

Fiedler Regularization: Learning Neural Networks with Graph Sparsity

Edric Tam, David Dunson

Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization

Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga

Quantized Decentralized Stochastic Learning over Directed Graphs

Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani

Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery

Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter

Multi-Agent Routing Value Iteration Network

Quinlan Sykora, Mengye Ren, Raquel Urtasun

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks

Jakub Swiatkowski, Kevin Roth, Bastiaan Veeling, Linh Tran, Joshua Dillon

Multi-objective Bayesian Optimization using Pareto-frontier Entropy

Shinya Suzuki, Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama

The Many Shapley Values for Model Explanation

Mukund Sundararajan, Amir Najmi

The Shapley Taylor Interaction Index

Mukund Sundararajan, Kedar Dhamdhere, Ashish Agarwal

An EM Approach to Non-autoregressive Conditional Sequence Generation

Zhiqing Sun, Yiming Yang

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros

Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking

Haoran Sun, Songtao Lu, Mingyi Hong

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, Jeffrey Clune

Adaptive Estimator Selection for Off-Policy Evaluation

Yi Su, Pavithra Srinath, Akshay Krishnamurthy

ConQUR: Mitigating Delusional Bias in Deep Q-Learning

DiJia Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier

Task Understanding from Confusing Multi-task Data

Xin Su, Yizhou Jiang, Shangqi Guo, Feng Chen

Doubly robust off-policy evaluation with shrinkage

Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík

Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks

David Stutz, Matthias Hein, Bernt Schiele

Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information

Karl Stratos, Sam Wiseman

Responsive Safety in Reinforcement Learning by PID Lagrangian Methods

Adam Stooke, Joshua Achiam, Pieter Abbeel

Which Tasks Should Be Learned Together in Multi-task Learning?

Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik

Robustness to Spurious Correlations via Human Annotations

Megha Srivastava, Tatsunori Hashimoto, Percy Liang

Hypernetwork approach to generating point clouds

Przemysław Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zieba

Provably Efficient Model-based Policy Adaptation

Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao

Bridging the Gap Between f-GANs and Wasserstein GANs

Jiaming Song, Stefano Ermon

Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation

Georgios Smyrnis, Petros Maragos

On the Generalization Benefit of Noise in Stochastic Gradient Descent

Samuel Smith, Erich Elsen, Soham De

When Explanations Lie: Why Many Modified BP Attributions Fail

Leon Sixt, Maximilian Granz, Tim Landgraf

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning

Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, Francois Fleuret

Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis

Vidyashankar Sivakumar, Steven Wu, Arindam Banerjee

Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure

John Sipple

Small-GAN: Speeding up GAN Training using Core-Sets

Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle

FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis

Aman Sinha, Matthew O’Kelly, Hongrui Zheng, Rahul Mangharam, John Duchi

Second-Order Provable Defenses against Adversarial Attacks

Sahil Singla, Soheil Feizi

Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise

Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gurbuzbalaban

Reinforcement Learning for Molecular Design Guided by Quantum Mechanics

Gregor Simm, Robert Pinsler, José Miguel Hernández-Lobato

A Generative Model for Molecular Distance Geometry

Gregor Simm, José Miguel Hernández-Lobato

Naive Exploration is Optimal for Online LQR

Max Simchowitz, Dylan Foster

Collaborative Machine Learning with Incentive-Aware Model Rewards

Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low

Deep Gaussian Markov Random Fields

Per Sidén, Fredrik Lindsten

Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards

Umer Siddique, Paul Weng, Matthieu Zimmer

Piecewise Linear Regression via a Difference of Convex Functions

Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama

Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits

Nian Si, Fan Zhang, Zhengyuan Zhou, Jose Blanchet

A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change

Salman Sadiq Shuvo, Yasin Yilmaz, Alan Bush, Mark Hafen

Predictive Coding for Locally-Linear Control

Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than

On Conditional Versus Marginal Bias in Multi-Armed Bandits

Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo

Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation

Wenxian Shi, Hao Zhou, Ning Miao, Lei Li

Informative Dropout for Robust Representation Learning: A Shape-bias Perspective

Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu

A Graph to Graphs Framework for Retrosynthesis Prediction

Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang

Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making

Chengchun Shi, Runzhe Wan, Rui Song, Wenbin Lu, Ling Leng

Message Passing Least Squares Framework and its Application to Rotation Synchronization

Yunpeng Shi, Gilad Lerman

Incremental Sampling Without Replacement for Sequence Models

Kensen Shi, David Bieber, Charles Sutton

Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks

Alexander Shevchenko, Marco Mondelli

One-shot Distributed Ridge Regression in High Dimensions

Yue Sheng, Edgar Dobriban

Extreme Multi-label Classification from Aggregated Labels

Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit Dhillon

PowerNorm: Rethinking Batch Normalization in Transformers

Sheng Shen, Zhewei Yao, Amir Gholami, Michael Mahoney, Kurt Keutzer

Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints

Cong Shen, Zhiyang Wang, Sofia Villar, Mihaela van der Schaar

Educating Text Autoencoders: Latent Representation Guidance via Denoising

Tianxiao Shen, Jonas Mueller, Dr.Regina Barzilay, Tommi Jaakkola

Deep Reinforcement Learning with Robust and Smooth Policy

Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao

PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions

Zhengyang Shen, Lingshen He, Zhouchen Lin, Jinwen Ma

Adaptive Sampling for Estimating Probability Distributions

Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh

Causal Strategic Linear Regression

Yonadav Shavit, Benjamin Edelman, Brian Axelrod

Lookahead-Bounded Q-learning

Ibrahim El Shar, Daniel Jiang

ControlVAE: Controllable Variational Autoencoder

Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu

Channel Equilibrium Networks for Learning Deep Representation

Wenqi Shao, Shitao Tang, Xingang Pan, Ping Tan, Xiaogang Wang

Evaluating Machine Accuracy on ImageNet

Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht

Learning Robot Skills with Temporal Variational Inference

Tanmay Shankar, Abhinav Gupta

Neural Kernels Without Tangents

Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Jonathan Ragan-Kelley

Optimistic Policy Optimization with Bandit Feedback

Lior Shani, Yonathan Efroni, Aviv Rosenberg, Shie Mannor

An Explicitly Relational Neural Network Architecture

Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David Barrett

Planning to Explore via Self-Supervised World Models

Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner

Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures

Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain Couillet

Universal Average-Case Optimality of Polyak Momentum

Damien Scieur, Fabian Pedregosa

Discriminative Adversarial Search for Abstractive Summarization

Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano

Off-Policy Actor-Critic with Shared Experience Replay

Simon Schmitt, Matteo Hessel, Karen Simonyan

Implicit competitive regularization in GANs

Florian Schaefer, Hongkai Zheng, Animashree Anandkumar

Harmonic Decompositions of Convolutional Networks

meyer scetbon, Zaïd Harchaoui

A Sample Complexity Separation between Non-Convex and Convex Meta-Learning

Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora

Constrained Markov Decision Processes via Backward Value Functions

harsh satija, Philip Amortila, Joelle Pineau

Detecting Out-of-Distribution Examples with Gram Matrices

Chandramouli Shama Sastry, Sageev Oore

Explicit Gradient Learning for Black-Box Optimization

Elad Sarafian, Mor Sinay, Yoram Louzoun, Noa Agmon, Sarit Kraus

The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent

Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein