ICML 2021

1,183 papers

SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies

Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-fei

Model-based Reinforcement Learning for Continuous Control with Posterior Sampling

Ying Fan, Yifei Ming

Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks

Xuhui Fan, Bin Li, Yaqiong Li, Scott A. Sisson

Data augmentation for deep learning based accelerated MRI reconstruction with limited data

Zalan Fabian, Reinhard Heckel, Mahdi Soltanolkotabi

Weight-covariance alignment for adversarially robust neural networks

Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy Hospedales

Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data

Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang, Aditya Balu, Ethan Herron

Graph Mixture Density Networks

Federico Errica, Davide Bacciu, Alessio Micheli

Whitening for Self-Supervised Representation Learning

Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe

Revealing the Structure of Deep Neural Networks via Convex Duality

Tolga Ergen, Mert Pilanci

Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs

Tolga Ergen, Mert Pilanci

Implicit Bias of Linear RNNs

Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K Fletcher

Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations

Patrick Emami, Pan He, Sanjay Ranka, Anand Rangarajan

Provably Strict Generalisation Benefit for Equivariant Models

Bryn Elesedy, Sheheryar Zaidi

Self-Paced Context Evaluation for Contextual Reinforcement Learning

Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer

Confidence-Budget Matching for Sequential Budgeted Learning

Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor

Reinforcement Learning Under Moral Uncertainty

Adrien Ecoffet, Joel Lehman

Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics

Arkopal Dutt, Andrey Lokhov, Marc D Vuffray, Sidhant Misra

Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network

Zhibin Duan, Dongsheng Wang, Bo Chen, Chaojie Wang, wenchao chen

Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning

Yaqi Duan, Chi Jin, Zhiyuan Li

Learning Diverse-Structured Networks for Adversarial Robustness

Xuefeng Du, Jingfeng ZHANG, Bo Han, Tongliang Liu, Yu Rong

Estimating $α$-Rank from A Few Entries with Low Rank Matrix Completion

Yali Du, Xue Yan, Xu Chen, Jun Wang, Haifeng Zhang

Putting the “Learning" into Learning-Augmented Algorithms for Frequency Estimation

Elbert Du, Franklyn Wang, Michael Mitzenmacher

Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation

Cunxiao Du, Zhaopeng Tu, Jing Jiang

Improved Contrastive Divergence Training of Energy-Based Models

Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch

Bilinear Classes: A Structural Framework for Provable Generalization in RL

Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan

Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction

Radu Alexandru Dragomir, Mathieu Even, Hadrien Hendrikx

How rotational invariance of common kernels prevents generalization in high dimensions

Konstantin Donhauser, Mingqi Wu, Fanny Yang

Attention is not all you need: pure attention loses rank doubly exponentially with depth

Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas

Kernel-Based Reinforcement Learning: A Finite-Time Analysis

Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

On Energy-Based Models with Overparametrized Shallow Neural Networks

Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna

Estimation and Quantization of Expected Persistence Diagrams

Vincent Divol, Theo Lacombe

Coded-InvNet for Resilient Prediction Serving Systems

Tuan Dinh, Kangwook Lee

Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach

Tianyu Ding, Zhihui Zhu, René Vidal, Daniel P Robinson

XOR-CD: Linearly Convergent Constrained Structure Generation

Fan Ding, Jianzhu Ma, Jinbo Xu, Yexiang Xue

ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables

Aleksandar Dimitriev, Mingyuan Zhou

Context-Aware Online Collective Inference for Templated Graphical Models

Charles Dickens, Connor Pryor, Eriq Augustine, Alexander Miller, Lise Getoor

A Wasserstein Minimax Framework for Mixed Linear Regression

Theo Diamandis, Yonina Eldar, Alireza Fallah, Farzan Farnia, Asuman Ozdaglar

Learning Online Algorithms with Distributional Advice

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Ali Vakilian, Nikos Zarifis

Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time

Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni, Jessica Shi

On the Inherent Regularization Effects of Noise Injection During Training

Oussama Dhifallah, Yue Lu

Versatile Verification of Tree Ensembles

Laurens Devos, Wannes Meert, Jesse Davis

Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation

Sam Devlin, Raluca Georgescu, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga

Bayesian Optimization over Hybrid Spaces

Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa

Kernel Continual Learning

Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao, Cees Snoek

Heterogeneity for the Win: One-Shot Federated Clustering

Don Kurian Dennis, Tian Li, Virginia Smith

Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing

Yuan Deng, Sebastien Lahaie, Vahab Mirrokni, Song Zuo

Toward Better Generalization Bounds with Locally Elastic Stability

Zhun Deng, Hangfeng He, Weijie Su

What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?

Weijian Deng, Stephen Gould, Liang Zheng

Multidimensional Scaling: Approximation and Complexity

Erik Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John Urschel

Grid-Functioned Neural Networks

Javier Dehesa, Andrew Vidler, Julian Padget, Christof Lutteroth

Transfer-Based Semantic Anomaly Detection

Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen

High-Dimensional Gaussian Process Inference with Derivatives

Filip de Roos, Alexandra Gessner, Philipp Hennig

Adversarial Robustness Guarantees for Random Deep Neural Networks

Giacomo De Palma, Bobak Kiani, Seth Lloyd

Bayesian Deep Learning via Subnetwork Inference

Erik Daxberger, Eric Nalisnick, James U Allingham, Javier Antoran, José Miguel Hernández-Lobato

Diffusion Source Identification on Networks with Statistical Confidence

Quinlan E Dawkins, Tianxi Li, Haifeng Xu

Catformer: Designing Stable Transformers via Sensitivity Analysis

Jared Q Davis, Albert Gu, Krzysztof Choromanski, Tri Dao, Christopher Re

Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data

Deepesh Data, Suhas Diggavi

Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers

Jyotikrishna Dass, Rabi Mahapatra

Lipschitz normalization for self-attention layers with application to graph neural networks

George Dasoulas, Kevin Scaman, Aladin Virmaux

SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning

Lokesh Chandra Das, Myounggyu Won

Measuring Robustness in Deep Learning Based Compressive Sensing

Mohammad Zalbagi Darestani, Akshay S Chaudhari, Reinhard Heckel

Intermediate Layer Optimization for Inverse Problems using Deep Generative Models

Giannis Daras, Joseph Dean, Ajil Jalal, Alex Dimakis

BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders

Dominic Danks, Christopher Yau

Newton Method over Networks is Fast up to the Statistical Precision

Amir Daneshmand, Gesualdo Scutari, Pavel Dvurechensky, Alexander Gasnikov

Re-understanding Finite-State Representations of Recurrent Policy Networks

Mohamad H Danesh, Anurag Koul, Alan Fern, Saeed Khorram

Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation

Christopher R. Dance, Julien Perez, Théo Cachet

Convex Regularization in Monte-Carlo Tree Search

Tuan Q Dam, Carlo D’Eramo, Jan Peters, Joni Pajarinen

Sliced Iterative Normalizing Flows

Biwei Dai, Uros Seljak

Fixed-Parameter and Approximation Algorithms for PCA with Outliers

Yogesh Dahiya, Fedor Fomin, Fahad Panolan, Kirill Simonov

SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels

Kunal Dahiya, Ananye Agarwal, Deepak Saini, Gururaj K, Jian Jiao

A Tale of Two Efficient and Informative Negative Sampling Distributions

Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao

Offline Reinforcement Learning with Pseudometric Learning

Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Leonard Hussenot, Olivier Pietquin

Consistent regression when oblivious outliers overwhelm

Tommaso d’Orsi, Gleb Novikov, David Steurer

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases

Stéphane D’Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli

Dynamic Balancing for Model Selection in Bandits and RL

Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano

Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability

Mihaela Curmei, Sarah Dean, Benjamin Recht

Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning

Sebastian Curi, Ilija Bogunovic, Andreas Krause

ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael F P O’Boyle

GBHT: Gradient Boosting Histogram Transform for Density Estimation

Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin

Randomized Algorithms for Submodular Function Maximization with a k-System Constraint

Shuang Cui, Kai Han, Tianshuai Zhu, Jing Tang, Benwei Wu

Parameterless Transductive Feature Re-representation for Few-Shot Learning

Wentao Cui, Yuhong Guo

Mind the Box: l_1-APGD for Sparse Adversarial Attacks on Image Classifiers

Francesco Croce, Matthias Hein

Environment Inference for Invariant Learning

Elliot Creager, Joern-Henrik Jacobsen, Richard Zemel

Generalised Lipschitz Regularisation Equals Distributional Robustness

Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith

Explaining Time Series Predictions with Dynamic Masks

Jonathan Crabbe, Mihaela van der Schaar

Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering

Romain Couillet, Florent Chatelain, Nicolas Le Bihan

Characterizing Fairness Over the Set of Good Models Under Selective Labels

Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova

A Discriminative Technique for Multiple-Source Adaptation

Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang

Relative Deviation Margin Bounds

Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh

Fairness and Bias in Online Selection

Jose Correa, Andres Cristi, Paul Duetting, Ashkan Norouzi-Fard

Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet

Exploiting Shared Representations for Personalized Federated Learning

Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai

Concentric mixtures of Mallows models for top-k rankings: sampling and identifiability

Fabien Collas, Ekhine Irurozki

Correlation Clustering in Constant Many Parallel Rounds

Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Nikos Parotsidis

Improving Ultrametrics Embeddings Through Coresets

Vincent Cohen-Addad, Rémi De Joannis De Verclos, Guillaume Lagarde

Differentially-Private Clustering of Easy Instances

Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

Scaling Properties of Deep Residual Networks

Alain-Sam Cohen, Rama Cont, Alain Rossier, Renyuan Xu

Riemannian Convex Potential Maps

Samuel Cohen, Brandon Amos, Yaron Lipman

Phasic Policy Gradient

Karl W Cobbe, Jacob Hilton, Oleg Klimov, John Schulman

First-Order Methods for Wasserstein Distributionally Robust MDP

Julien Grand Clement, Christian Kroer