ICML 2021

1,183 papers

Deep Generative Learning via Schrödinger Bridge

Gefei Wang, Yuling Jiao, Qian Xu, Yang Wang, Can Yang

SG-PALM: a Fast Physically Interpretable Tensor Graphical Model

Yu Wang, Alfred Hero

Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time

Weichen Wang, Jiequn Han, Zhuoran Yang, Zhaoran Wang

AlphaNet: Improved Training of Supernets with Alpha-Divergence

Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra

Label Distribution Learning Machine

Jing Wang, Xin Geng

Self-Tuning for Data-Efficient Deep Learning

Ximei Wang, Jinghan Gao, Mingsheng Long, Jianmin Wang

Explainable Automated Graph Representation Learning with Hyperparameter Importance

Xin Wang, Shuyi Fan, Kun Kuang, Wenwu Zhu

Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework

Wenxiao Wang, Minghao Chen, Shuai Zhao, Long Chen, Jinming Hu

Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss

Jiali Wang, He Chen, Rujun Jiang, Xudong Li, Zihao Li

A Proxy Variable View of Shared Confounding

Yixin Wang, David Blei

Fairness of Exposure in Stochastic Bandits

Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims

Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model

Zi Wang

Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces

Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu

Learning and Planning in Average-Reward Markov Decision Processes

Yi Wan, Abhishek Naik, Richard S Sutton

Task-Optimal Exploration in Linear Dynamical Systems

Andrew J Wagenmaker, Max Simchowitz, Kevin Jamieson

Safe Reinforcement Learning Using Advantage-Based Intervention

Nolan C Wagener, Byron Boots, Ching-An Cheng

Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization

Neha Wadia, Daniel Duckworth, Samuel S Schoenholz, Ethan Dyer, Jascha Sohl-dickstein

Principal Component Hierarchy for Sparse Quadratic Programs

Robbie Vreugdenhil, Viet Anh Nguyen, Armin eftekhari, Peyman Mohajerin Esfahani

Object Segmentation Without Labels with Large-Scale Generative Models

Andrey Voynov, Stanislav Morozov, Artem Babenko

Efficient Training of Robust Decision Trees Against Adversarial Examples

Daniël Vos, Sicco Verwer

Neuro-algorithmic Policies Enable Fast Combinatorial Generalization

Marin Vlastelica, Michal Rolinek, Georg Martius

Online Graph Dictionary Learning

Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies

Paul Vicol, Luke Metz, Jascha Sohl-dickstein

Sparsifying Networks via Subdifferential Inclusion

Sagar Verma, Jean-Christophe Pesquet

Towards Domain-Agnostic Contrastive Learning

Vikas Verma, Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc Le

CURI: A Benchmark for Productive Concept Learning Under Uncertainty

Ramakrishna Vedantam, arthur szlam, Maximillian Nickel, Ari Morcos, Brenden M Lake

Active Deep Probabilistic Subsampling

Hans Van Gorp, Iris Huijben, Bastiaan S Veeling, Nicola Pezzotti, Ruud J. G. Van Sloun

LTL2Action: Generalizing LTL Instructions for Multi-Task RL

Pashootan Vaezipoor, Andrew C Li, Rodrigo A Toro Icarte, Sheila A. McIlraith

SGLB: Stochastic Gradient Langevin Boosting

Aleksei Ustimenko, Liudmila Prokhorenkova

Fast Projection Onto Convex Smooth Constraints

Ilnura Usmanova, Maryam Kamgarpour, Andreas Krause, Kfir Levy

A Framework for Private Matrix Analysis in Sliding Window Model

Jalaj Upadhyay, Sarvagya Upadhyay

PixelTransformer: Sample Conditioned Signal Generation

Shubham Tulsiani, Abhinav Gupta

Cumulants of Hawkes Processes are Robust to Observation Noise

William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran

Provable Meta-Learning of Linear Representations

Nilesh Tripuraneni, Chi Jin, Michael Jordan

Learning a Universal Template for Few-shot Dataset Generalization

Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin

A New Formalism, Method and Open Issues for Zero-Shot Coordination

Johannes Treutlein, Michael Dennis, Caspar Oesterheld, Jakob Foerster

On Disentangled Representations Learned from Correlated Data

Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi

Bayesian Optimistic Optimisation with Exponentially Decaying Regret

Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh

SMG: A Shuffling Gradient-Based Method with Momentum

Trang H Tran, Lam M Nguyen, Quoc Tran-Dinh

Sparse within Sparse Gaussian Processes using Neighbor Information

Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone

Conservative Objective Models for Effective Offline Model-Based Optimization

Brandon Trabucco, Aviral Kumar, XINYANG GENG, Sergey Levine

Training data-efficient image transformers & distillation through attention

Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles

Diffusion Earth Mover’s Distance and Distribution Embeddings

Alexander Y Tong, Guillaume Huguet, Amine Natik, Kincaid MacDonald, Manik Kuchroo

Deep Continuous Networks

Nergis Tomen, Silvia-Laura Pintea, Jan van Gemert

Probabilistic Programs with Stochastic Conditioning

David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang

Nonparametric Decomposition of Sparse Tensors

Conor Tillinghast, Shandian Zhe

BORE: Bayesian Optimization by Density-Ratio Estimation

Louis C Tiao, Aaron Klein, Matthias W Seeger, Edwin V. Bonilla, Cédric Archambeau

Online Learning in Unknown Markov Games

Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra

Understanding self-supervised learning dynamics without contrastive pairs

Yuandong Tian, Xinlei Chen, Surya Ganguli

Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model

Christian B Thygesen, Christian Skjødt Steenmans, Ahmad Salim Al-Sibahi, Lys Sanz Moreta, Anders Bundgård Sørensen

Monte Carlo Variational Auto-Encoders

Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines

Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism

Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael Jordan, Ken Goldberg

Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers

Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C Mozer

Moreau-Yosida f-divergences

Dávid Terjék

T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP

Jiaye Teng, Zeren Tan, Yang Yuan

OmniNet: Omnidirectional Representations from Transformers

Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Gupta, Philip M Pham

Synthesizer: Rethinking Self-Attention for Transformer Models

Yi Tay, Dara Bahri, Donald Metzler, Da-Cheng Juan, Zhe Zhao

A Language for Counterfactual Generative Models

Zenna Tavares, James Koppel, Xin Zhang, Ria Das, Armando Solar-Lezama

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Bahar Taskesen, Man-Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen

Understanding the Dynamics of Gradient Flow in Overparameterized Linear models

Salma Tarmoun, Guilherme Franca, Benjamin D Haeffele, René Vidal

REPAINT: Knowledge Transfer in Deep Reinforcement Learning

Yunzhe Tao, Sahika Genc, Jonathan Chung, Tao Sun, Sunil Mallya

Taylor Expansion of Discount Factors

Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko

1-bit Adam: Communication Efficient Large-Scale Training with Adam’s Convergence Speed

Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li

SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples

Anshoo Tandon, Aldric Han, Vincent Tan

EfficientNetV2: Smaller Models and Faster Training

Mingxing Tan, Quoc Le

Supervised Tree-Wasserstein Distance

Yuki Takezawa, Ryoma Sato, Makoto Yamada

Approximation Theory Based Methods for RKHS Bandits

Sho Takemori, Masahiro Sato

Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training

Kai Sheng Tai, Peter D Bailis, Gregory Valiant

DriftSurf: Stable-State / Reactive-State Learning under Concept Drift

Ashraf Tahmasbi, Ellango Jothimurugesan, Srikanta Tirthapura, Phillip B Gibbons

Robust Representation Learning via Perceptual Similarity Metrics

Saeid A Taghanaki, Kristy Choi, Amir Hosein Khasahmadi, Anirudh Goyal

Parallel tempering on optimized paths

Saifuddin Syed, Vittorio Romaniello, Trevor Campbell, Alexandre Bouchard-côté

Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu

Generalization Error Bound for Hyperbolic Ordinal Embedding

Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi

Model-Targeted Poisoning Attacks with Provable Convergence

Fnu Suya, Saeed Mahloujifar, Anshuman Suri, David Evans, Yuan Tian

Reinforcement Learning for Cost-Aware Markov Decision Processes

Wesley Suttle, Kaiqing Zhang, Zhuoran Yang, Ji Liu, David Kraemer

PAC-Learning for Strategic Classification

Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao

Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

Haitian Sun, Patrick Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William W Cohen

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition

Shengyang Sun, Jiaxin Shi, Andrew Gordon Gordon Wilson, Roger B Grosse

DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

Wei-Fang Sun, Cheng-Kuang Lee, Chun-Yi Lee

What Makes for End-to-End Object Detection?

Peize Sun, Yi Jiang, Enze Xie, Wenqi Shao, Zehuan Yuan

AutoSampling: Search for Effective Data Sampling Schedules

Ming Sun, Haoxuan Dou, Baopu Li, Junjie Yan, Wanli Ouyang

Nondeterminism and Instability in Neural Network Optimization

Cecilia Summers, Michael J. Dinneen

Not All Memories are Created Equal: Learning to Forget by Expiring

Sainbayar Sukhbaatar, Da JU, Spencer Poff, Stephen Roller, arthur szlam

More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method

Kazuya Sugiyama, Vo Nguyen Le Duy, Ichiro Takeuchi

K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets

Xiu Su, Shan You, Mingkai Zheng, Fei Wang, Chen Qian

Decoupling Representation Learning from Reinforcement Learning

Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin

Decomposed Mutual Information Estimation for Contrastive Representation Learning

Alessandro Sordoni, Nouha Dziri, Hannes Schulz, Geoff Gordon, Philip Bachman

Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning

Sumedh A Sontakke, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf

Oblivious Sketching-based Central Path Method for Linear Programming

Zhao Song, zheng Yu

Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums

Chaobing Song, Stephen J Wright, Jelena Diakonikolas

Fast Sketching of Polynomial Kernels of Polynomial Degree

Zhao Song, David Woodruff, zheng Yu, Lichen Zhang

PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration

Yuda Song, Wen Sun

Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving

Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon

Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks

Sungryull Sohn, Sungtae Lee, Jongwook Choi, Harm H Van Seijen, Mehdi Fatemi

Multi-Task Reinforcement Learning with Context-based Representations

Shagun Sodhani, Amy Zhang, Joelle Pineau

Skew Orthogonal Convolutions

Sahil Singla, Soheil Feizi

Structured World Belief for Reinforcement Learning in POMDP

Gautam Singh, Skand Peri, Junghyun Kim, Hyunseok Kim, Sungjin Ahn

Flow-based Attribution in Graphical Models: A Recursive Shapley Approach

Raghav Singal, George Michailidis, Hoiyi Ng

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Berfin Simsek, François Ged, Arthur Jacot, Francesco Spadaro, Clement Hongler

PopSkipJump: Decision-Based Attack for Probabilistic Classifiers

Carl-Johann Simon-Gabriel, Noman Ahmed Sheikh, Andreas Krause