ICML 2020

1,084 papers

Adversarial Robustness for Code

Pavol Bielik, Martin Vechev

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders

Ioana Bica, Ahmed Alaa, Mihaela van der Schaar

Spectral Clustering with Graph Neural Networks for Graph Pooling

Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi

Low-Rank Bottleneck in Multi-head Attention Models

Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar

Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures

Chiranjib Bhattacharyya, Ravindran Kannan

Learning and Sampling of Atomic Interventions from Observations

Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, Vinodchandran N. Variyam

When are Non-Parametric Methods Robust?

Robi Bhattacharjee, Kamalika Chaudhuri

Online Learning with Imperfect Hints

Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

Implicit differentiation of Lasso-type models for hyperparameter optimization

Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort

Training Neural Networks for and by Interpolation

Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

Efficient Policy Learning from Surrogate-Loss Classification Reductions

Andrew Bennett, Nathan Kallus

Preselection Bandits

Viktor Bengs, Eyke Hüllermeier

Interference and Generalization in Temporal Difference Learning

Emmanuel Bengio, Joelle Pineau, Doina Precup

Defense Through Diverse Directions

Christopher Bender, Yang Li, Yifeng Shi, Michael K. Reiter, Junier Oliva

The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers

Pierre Bellec, Dana Yang

Decoupled Greedy Learning of CNNs

Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon

Kernel interpolation with continuous volume sampling

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais

On Second-Order Group Influence Functions for Black-Box Predictions

Samyadeep Basu, Xuchen You, Soheil Feizi

ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications

Kinjal Basu, Amol Ghoting, Rahul Mazumder, Yao Pan

Private Query Release Assisted by Public Data

Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman

Frequency Bias in Neural Networks for Input of Non-Uniform Density

Ronen Basri, Meirav Galun, Amnon Geifman, David Jacobs, Yoni Kasten

Learning the piece-wise constant graph structure of a varying Ising model

Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis

Option Discovery in the Absence of Rewards with Manifold Analysis

Amitay Bar, Ronen Talmon, Ron Meir

Fast OSCAR and OWL Regression via Safe Screening Rules

Runxue Bao, Bin Gu, Heng Huang

UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang

Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters

Subho Banerjee, Saurabh Jha, Zbigniew Kalbarczyk, Ravishankar Iyer

Dual Mirror Descent for Online Allocation Problems

Santiago Balseiro, Haihao Lu, Vahab Mirrokni

Stochastic Optimization for Regularized Wasserstein Estimators

Marin Ballu, Quentin Berthet, Francis Bach

Ready Policy One: World Building Through Active Learning

Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts

Refined bounds for algorithm configuration: The knife-edge of dual class approximability

Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik

Coresets for Clustering in Graphs of Bounded Treewidth

Daniel Baker, Vladimir braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer

Sparse Subspace Clustering with Entropy-Norm

Liang Bai, Jiye Liang

Provable Self-Play Algorithms for Competitive Reinforcement Learning

Yu Bai, Chi Jin

Deep k-NN for Noisy Labels

Dara Bahri, Heinrich Jiang, Maya Gupta

Learning De-biased Representations with Biased Representations

Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, Seong Joon Oh

Fiduciary Bandits

Gal Bahar, Omer Ben-Porat, Kevin Leyton-brown, Moshe Tennenholtz

Agent57: Outperforming the Atari Human Benchmark

Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi

Scalable Nearest Neighbor Search for Optimal Transport

Arturs Backurs, Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner

Constant Curvature Graph Convolutional Networks

Gregor Bachmann, Gary Becigneul, Octavian Ganea

Forecasting Sequential Data Using Consistent Koopman Autoencoders

Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael Mahoney

Model-Based Reinforcement Learning with Value-Targeted Regression

Alex Ayoub, Zeyu Jia, Csaba Szepesvári, Mengdi Wang, Lin Yang

Sparse Convex Optimization via Adaptively Regularized Hard Thresholding

Kyriakos Axiotis, Maxim Sviridenko

Sample Amplification: Increasing Dataset Size even when Learning is Impossible

Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant

Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks

Pranjal Awasthi, Natalie Frank, Mehryar Mohri

Safe screening rules for L0-regression from Perspective Relaxations

Alper Atamturk, Andres Gomez

On the Convergence of Nesterov’s Accelerated Gradient Method in Stochastic Settings

Mahmoud Assran, Mike Rabbat

Invertible generative models for inverse problems: mitigating representation error and dataset bias

Muhammad Asim, Mara Daniels, Oscar Leong, Ali Ahmed, Paul Hand

Black-box Certification and Learning under Adversarial Perturbations

Hassan Ashtiani, Vinayak Pathak, Ruth Urner

Quantum Boosting

Srinivasan Arunachalam, Reevu Maity

Provable Representation Learning for Imitation Learning via Bi-level Optimization

Sanjeev Arora, Simon Du, Sham Kakade, Yuping Luo, Nikunj Saunshi

NADS: Neural Architecture Distribution Search for Uncertainty Awareness

Randy Ardywibowo, Shahin Boluki, Xinyu Gong, Zhangyang Wang, Xiaoning Qian

Online metric algorithms with untrusted predictions

Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, Bertrand Simon

Low-loss connection of weight vectors: distribution-based approaches

Ivan Anokhin, Dmitry Yarotsky

Population-Based Black-Box Optimization for Biological Sequence Design

Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan

Fairwashing explanations with off-manifold detergent

Christopher Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel

Customizing ML Predictions for Online Algorithms

Keerti Anand, Rong Ge, Debmalya Panigrahi

The Differentiable Cross-Entropy Method

Brandon Amos, Denis Yarats

Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"

Saeed Amizadeh, Hamid Palangi, Alex Polozov, Yichen Huang, Kazuhito Koishida

Discount Factor as a Regularizer in Reinforcement Learning

Ron Amit, Ron Meir, Kamil Ciosek

LowFER: Low-rank Bilinear Pooling for Link Prediction

Saadullah Amin, Stalin Varanasi, Katherine Ann Dunfield, Günter Neumann

Structural Language Models of Code

Uri Alon, Roy Sadaka, Omer Levy, Eran Yahav

The Implicit Regularization of Stochastic Gradient Flow for Least Squares

Alnur Ali, Edgar Dobriban, Ryan Tibshirani

Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

Amr Alexandari, Anshul Kundaje, Avanti Shrikumar

Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay

Reda Alami, Odalric Maillard, Raphaël Féraud

A new regret analysis for Adam-type algorithms

Ahmet Alacaoglu, Yura Malitsky, Panayotis Mertikopoulos, Volkan Cevher

Random extrapolation for primal-dual coordinate descent

Ahmet Alacaoglu, Olivier Fercoq, Volkan Cevher

Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions

Ahmed Alaa, Mihaela van der Schaar

Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions

Ahmed Alaa, Mihaela van der Schaar

Why bigger is not always better: on finite and infinite neural networks

Laurence Aitchison

Invariant Risk Minimization Games

Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, Amit Dhurandhar

Learning What to Defer for Maximum Independent Sets

Sungsoo Ahn, Younggyo Seo, Jinwoo Shin

LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments

Ali Ahmaditeshnizi, Saber Salehkaleybar, Negar Kiyavash

Optimal Bounds between f-Divergences and Integral Probability Metrics

Rohit Agrawal, Thibaut Horel

An Optimistic Perspective on Offline Reinforcement Learning

Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi

Boosting for Control of Dynamical Systems

Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu

Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions

Arpit Agarwal, Shivani Agarwal, Sanjeev Khanna, Prathamesh Patil

The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization

Ben Adlam, Jeffrey Pennington

Efficient Intervention Design for Causal Discovery with Latents

Raghavendra Addanki, Shiva Kasiviswanathan, Andrew McGregor, Cameron Musco

Context Aware Local Differential Privacy

Jayadev Acharya, Kallista Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun

A Geometric Approach to Archetypal Analysis via Sparse Projections

Vinayak Abrol, Pulkit Sharma

Super-efficiency of automatic differentiation for functions defined as a minimum

Pierre Ablin, Gabriel Peyré, Thomas Moreau

Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation

Marc Abeille, Alessandro Lazaric

A distributional view on multi-objective policy optimization

Abbas Abdolmaleki, Sandy Huang, Leonard Hasenclever, Michael Neunert, Francis Song

Selective Dyna-Style Planning Under Limited Model Capacity

Zaheer Abbas, Samuel Sokota, Erin Talvitie, Martha White