ICML 2021

1,183 papers

GeomCA: Geometric Evaluation of Data Representations

Petra Poklukar, Anastasiia Varava, Danica Kragic

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs

Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines

Towards Practical Mean Bounds for Small Samples

My Phan, Philip Thomas, Erik Learned-Miller

Megaverse: Simulating Embodied Agents at One Million Experiences per Second

Aleksei Petrenko, Erik Wijmans, Brennan Shacklett, Vladlen Koltun

Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision

Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen

Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders

Adeel Pervez, Efstratios Gavves

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland

Modelling Behavioural Diversity for Learning in Open-Ended Games

Nicolas Perez-Nieves, Yaodong Yang, Oliver Slumbers, David H Mguni, Ying Wen

Rissanen Data Analysis: Examining Dataset Characteristics via Description Length

Ethan Perez, Douwe Kiela, Kyunghyun Cho

Privacy-Preserving Video Classification with Convolutional Neural Networks

Sikha Pentyala, Rafael Dowsley, Martine De Cock

How could Neural Networks understand Programs?

Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He

Homomorphic Sensing: Sparsity and Noise

Liangzu Peng, Boshi Wang, Manolis Tsakiris

Ensemble Bootstrapping for Q-Learning

Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir

CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints

Anselm Paulus, Michal Rolinek, Vít Musil, Brandon Amos, Georg Martius

PHEW : Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data

Shreyas Malakarjun Patil, Constantine Dovrolis

Optimal Counterfactual Explanations in Tree Ensembles

Axel Parmentier, Thibaut Vidal

Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation

Sung Woo Park, Dong Wook Shu, Junseok Kwon

Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression

Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet

Unsupervised Representation Learning via Neural Activation Coding

Yookoon Park, Sangho Lee, Gunhee Kim, David Blei

Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data

Sung Woo Park, Junseok Kwon

Leveraging Good Representations in Linear Contextual Bandits

Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta

Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification

Bo Pang, Ying Nian Wu

Inference for Network Regression Models with Community Structure

Mengjie Pan, Tyler Mccormick, Bailey Fosdick

RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting

Soumyasundar Pal, Liheng Ma, Yingxue Zhang, Mark Coates

Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics

Avik Pal, Yingbo Ma, Viral Shah, Christopher V Rackauckas

Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling

Ozan Özdenizci, Robert Legenstein

Vector Quantized Models for Planning

Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aaron van den Oord

Generalization Guarantees for Neural Architecture Search with Train-Validation Split

Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi

Autoencoder Image Interpolation by Shaping the Latent Space

Alon Oring, Zohar Yakhini, Yacov Hel-Or

Sparsity-Agnostic Lasso Bandit

Min-hwan Oh, Garud Iyengar, Assaf Zeevi

Regularizing towards Causal Invariance: Linear Models with Proxies

Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag

Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes

Sebastian W Ober, Laurence Aitchison

Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods

Chris Nota, Philip Thomas, Bruno C. da Silva

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting

Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni

The Impact of Record Linkage on Learning from Feature Partitioned Data

Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo

WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points

Albert No, TaeHo Yoon, Kwon Sehyun, Ernest K Ryu

Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction

Kenta Niwa, Guoqiang Zhang, W. Bastiaan Kleijn, Noboru Harada, Hiroshi Sawada

AdaXpert: Adapting Neural Architecture for Growing Data

Shuaicheng Niu, Jiaxiang Wu, Guanghui Xu, Yifan Zhang, Yong Guo

Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications

Sloan Nietert, Ziv Goldfeld, Kengo Kato

Improved Denoising Diffusion Probabilistic Models

Alexander Quinn Nichol, Prafulla Dhariwal

Data Augmentation for Meta-Learning

Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein

Differentially Private Densest Subgraph Detection

Dung Nguyen, Anil Vullikanti

Temporal Predictive Coding For Model-Based Planning In Latent Space

Tung D Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon

Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks

Quynh Nguyen, Marco Mondelli, Guido F Montufar

Nonmyopic Multifidelity Acitve Search

Quan Nguyen, Arghavan Modiri, Roman Garnett

Interactive Learning from Activity Description

Khanh X Nguyen, Dipendra Misra, Robert Schapire, Miroslav Dudík, Patrick Shafto

Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search

Vu Nguyen, Tam Le, Makoto Yamada, Michael A. Osborne

Cross-model Back-translated Distillation for Unsupervised Machine Translation

Xuan-Phi Nguyen, Shafiq Joty, Thanh-Tung Nguyen, Kui Wu, Ai Ti Aw

Value-at-Risk Optimization with Gaussian Processes

Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths

Quynh Nguyen

Incentivizing Compliance with Algorithmic Instruments

Dung Daniel T Ngo, Logan Stapleton, Vasilis Syrgkanis, Steven Wu

Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners

Elias Chaibub Neto

Policy Caches with Successor Features

Mark Nemecek, Ronald Parr

Continuous Coordination As a Realistic Scenario for Lifelong Learning

Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, Sarath Chandar

Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information

Willie Neiswanger, Ke Alexander Wang, Stefano Ermon

Emergent Social Learning via Multi-agent Reinforcement Learning

Kamal K Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques

HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search

Niv Nayman, Yonathan Aflalo, Asaf Noy, Lihi Zelnik

Geometric convergence of elliptical slice sampling

Viacheslav Natarovskii, Daniel Rudolf, Björn Sprungk

Generating images with sparse representations

Charlie Nash, Jacob Menick, Sander Dieleman, Peter Battaglia

Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering

Shyam Narayanan, Sandeep Silwal, Piotr Indyk, Or Zamir

Memory-Efficient Pipeline-Parallel DNN Training

Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia

GMAC: A Distributional Perspective on Actor-Critic Framework

Daniel W Nam, Younghoon Kim, Chan Y Park

Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding

Akira Nakagawa, Keizo Kato, Taiji Suzuki

Online Limited Memory Neural-Linear Bandits with Likelihood Matching

Ofir Nabati, Tom Zahavy, Shie Mannor

No-regret Algorithms for Capturing Events in Poisson Point Processes

Mojmir Mutny, Andreas Krause

Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold

Kieran A Murphy, Carlos Esteves, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia

Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning

Tomoya Murata, Taiji Suzuki

Oblivious Sketching for Logistic Regression

Alexander Munteanu, Simon Omlor, David Woodruff

Outlier-Robust Optimal Transport

Debarghya Mukherjee, Aritra Guha, Justin M Solomon, Yuekai Sun, Mikhail Yurochkin

Connecting Interpretability and Robustness in Decision Trees through Separation

Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri

Neural Rough Differential Equations for Long Time Series

James Morrill, Cristopher Salvi, Patrick Kidger, James Foster

Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games

Dustin Morrill, Ryan D’Orazio, Marc Lanctot, James R Wright, Michael Bowling

PODS: Policy Optimization via Differentiable Simulation

Miguel Angel Zamora Mora, Momchil Peychev, Sehoon Ha, Martin Vechev, Stelian Coros

The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization

Taiki Miyagawa, Akinori F Ebihara

Offline Meta-Reinforcement Learning with Advantage Weighting

Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn

An Identifiable Double VAE For Disentangled Representations

Graziano Mita, Maurizio Filippone, Pietro Michiardi

On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks

Hancheng Min, Salma Tarmoun, René Vidal, Enrique Mallada

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

Dongchan Min, Dong Bok Lee, Eunho Yang, Sung Ju Hwang

Signatured Deep Fictitious Play for Mean Field Games with Common Noise

Ming Min, Ruimeng Hu

Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh

Outside the Echo Chamber: Optimizing the Performative Risk

John P Miller, Juan C Perdomo, Tijana Zrnic

EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture

Chenfeng Miao, Liang Shuang, Zhengchen Liu, Chen Minchuan, Jun Ma

Learning in Nonzero-Sum Stochastic Games with Potentials

David H Mguni, Yutong Wu, Yali Du, Yaodong Yang, Ziyi Wang

Mixed Nash Equilibria in the Adversarial Examples Game

Laurent Meunier, meyer scetbon, Rafael B Pinot, Jamal Atif, Yann Chevaleyre

Provably Efficient Learning of Transferable Rewards

Alberto Maria Metelli, Giorgia Ramponi, Alessandro Concetti, Marcello Restelli

Counterfactual Credit Assignment in Model-Free Reinforcement Learning

Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade

Learn2Hop: Learned Optimization on Rough Landscapes

Amil Merchant, Luke Metz, Samuel S Schoenholz, Ekin D Cubuk

A statistical perspective on distillation

Aditya K Menon, Ankit Singh Rawat, Sashank Reddi, Seungyeon Kim, Sanjiv Kumar

An Integer Linear Programming Framework for Mining Constraints from Data

Tao Meng, Kai-Wei Chang

UCB Momentum Q-learning: Correcting the bias without forgetting

Pierre Menard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko

Fast active learning for pure exploration in reinforcement learning

Pierre Menard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent

Neural Architecture Search without Training

Joe Mellor, Jack Turner, Amos Storkey, Elliot J Crowley

A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions

Gabriel Mel, Surya Ganguli

Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks

Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik

Leveraging Non-uniformity in First-order Non-convex Optimization

Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvári, Dale Schuurmans

Fundamental Tradeoffs in Distributionally Adversarial Training

Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi, Anup Rao, Tung Mai

Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees

Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen H Bach, Eli Upfal

Robust Unsupervised Learning via L-statistic Minimization

Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction

Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva

Necessary and sufficient conditions for causal feature selection in time series with latent common causes

Atalanti A Mastakouri, Bernhard Schölkopf, Dominik Janzing