AISTATS 2021

455 papers

Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency

Yuyang Deng, Mehrdad Mahdavi

Hyperparameter Transfer Learning with Adaptive Complexity

Samuel Horváth, Aaron Klein, Peter Richtarik, Cédric Archambeau

On Data Efficiency of Meta-learning

Maruan Al-Shedivat, Liam Li, Eric Xing, Ameet Talwalkar

Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time

Alan Kuhnle

Momentum Improves Optimization on Riemannian Manifolds

Foivos Alimisis, Antonio Orvieto, Gary Becigneul, Aurelien Lucchi

Reaping the Benefits of Bundling under High Production Costs

Will Ma, David Simchi-Levi

Latent variable modeling with random features

Gregory Gundersen, Michael Zhang, Barbara Engelhardt

Stable ResNet

Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet

SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation

Robert Gower, Othmane Sebbouh, Nicolas Loizou

Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence

Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji, Simon Lacoste-Julien

Inference in Stochastic Epidemic Models via Multinomial Approximations

Nick Whiteley, Lorenzo Rimella

Anderson acceleration of coordinate descent

Quentin Bertrand, Mathurin Massias

Differentially Private Online Submodular Maximization

Sebastian Perez Salazar, Rachel Cummings

Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions

Kartik Ahuja, Karthikeyan Shanmugam, Amit Dhurandhar

A Study of Condition Numbers for First-Order Optimization

Charles Guille-Escuret, Manuela Girotti, Baptiste Goujaud, Ioannis Mitliagkas

Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship

Abishek Sankararaman, Soumya Basu, Karthik Abinav Sankararaman

Goodness-of-Fit Test for Mismatched Self-Exciting Processes

Song Wei, Shixiang Zhu, Minghe Zhang, Yao Xie

Gaming Helps! Learning from Strategic Interactions in Natural Dynamics

Yahav Bechavod, Katrina Ligett, Steven Wu, Juba Ziani

Maximal Couplings of the Metropolis-Hastings Algorithm

Guanyang Wang, John O’Leary, Pierre Jacob

Provably Safe PAC-MDP Exploration Using Analogies

Melrose Roderick, Vaishnavh Nagarajan, Zico Kolter

Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes

Nhuong Nguyen, Toan Nguyen, PHUONG HA NGUYEN, Quoc Tran-Dinh, Lam Nguyen

Latent Derivative Bayesian Last Layer Networks

Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters

Finding First-Order Nash Equilibria of Zero-Sum Games with the Regularized Nikaido-Isoda Function

Ioannis Tsaknakis, Mingyi Hong

Learning with Hyperspherical Uniformity

Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf

Differentially Private Analysis on Graph Streams

Jalaj Upadhyay, Sarvagya Upadhyay, Raman Arora

Abstract Value Iteration for Hierarchical Reinforcement Learning

Kishor Jothimurugan, Osbert Bastani, Rajeev Alur

On Learning Continuous Pairwise Markov Random Fields

Abhin Shah, Devavrat Shah, Gregory Wornell

Algorithms for Fairness in Sequential Decision Making

Min Wen, Osbert Bastani, Ufuk Topcu

Consistent k-Median: Simpler, Better and Robust

Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian

Online k-means Clustering

Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom

When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence

Ziwei Guan, Tengyu Xu, Yingbin Liang

Top-m identification for linear bandits

Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez

Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings

Jean-Francois Ton, Lucian CHAN, Yee Whye Teh, Dino Sejdinovic

Linearly Constrained Gaussian Processes with Boundary Conditions

Markus Lange-Hegermann

Transforming Gaussian Processes With Normalizing Flows

Juan Maroñas, Oliver Hamelijnck, Jeremias Knoblauch, Theodoros Damoulas

A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix

Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure, Guillaume Rabusseau, Pierre Alquier

Unifying Clustered and Non-stationary Bandits

Chuanhao Li, Qingyun Wu, Hongning Wang

Measure Transport with Kernel Stein Discrepancy

Matthew Fisher, Tui Nolan, Matthew Graham, Dennis Prangle, Chris Oates

The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers

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

Aligning Time Series on Incomparable Spaces

Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Deisenroth

Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy

Onur Teymur, Jackson Gorham, Marina Riabiz, Chris Oates

On Riemannian Stochastic Approximation Schemes with Fixed Step-Size

Alain Durmus, Pablo Jiménez, Eric Moulines, Salem SAID

Optimizing Percentile Criterion using Robust MDPs

Bahram Behzadian, Reazul Hasan Russel, Marek Petrik, Chin Pang Ho

ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning

Martin Royer, Frederic Chazal, Clément Levrard, Yuhei Umeda, Yuichi Ike

Stochastic Linear Bandits Robust to Adversarial Attacks

Ilija Bogunovic, Arpan Losalka, Andreas Krause, Jonathan Scarlett

Hadamard Wirtinger Flow for Sparse Phase Retrieval

Fan Wu, Patrick Rebeschini

Mean-Variance Analysis in Bayesian Optimization under Uncertainty

Shogo Iwazaki, Yu Inatsu, Ichiro Takeuchi

Adaptive Sampling for Fast Constrained Maximization of Submodular Functions

Francesco Quinzan, Vanja Doskoc, Andreas Göbel, Tobias Friedrich

Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions

Shunsuke Horii

Regret Minimization for Causal Inference on Large Treatment Space

Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima

Interpretable Random Forests via Rule Extraction

Clement Benard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet

Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model

Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong

Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers

Alex Lamb, Anirudh Goyal, Agnieszka Słowik, Michael Mozer, Philippe Beaudoin

On the High Accuracy Limitation of Adaptive Property Estimation

Yanjun Han

Parametric Programming Approach for More Powerful and General Lasso Selective Inference

Vo Nguyen Le Duy, Ichiro Takeuchi

Learning with risk-averse feedback under potentially heavy tails

Matthew Holland, El Mehdi Haress

Provable Hierarchical Imitation Learning via EM

Zhiyu Zhang, Ioannis Paschalidis

Understanding the wiring evolution in differentiable neural architecture search

Sirui Xie, Shoukang Hu, Xinjiang Wang, Chunxiao Liu, Jianping Shi

Robustness and scalability under heavy tails, without strong convexity

Matthew Holland

Deep Fourier Kernel for Self-Attentive Point Processes

Shixiang Zhu, Minghe Zhang, Ruyi Ding, Yao Xie

Multi-Fidelity High-Order Gaussian Processes for Physical Simulation

Zheng Wang, Wei Xing, Robert Kirby, Shandian Zhe

Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning

Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang

Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network

Tianyang Hu, Wenjia Wang, Cong Lin, Guang Cheng

Learning Prediction Intervals for Regression: Generalization and Calibration

Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang

Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms

Tengyu Xu, Yingbin Liang

A constrained risk inequality for general losses

John Duchi, Feng Ruan

Efficient Computation and Analysis of Distributional Shapley Values

Yongchan Kwon, Manuel A. Rivas, James Zou

Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints

Robin Vogel, Aurélien Bellet, Stéphan Clémençon

An Analysis of the Adaptation Speed of Causal Models

Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien

Nested Barycentric Coordinate System as an Explicit Feature Map

Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele

Sharp Analysis of a Simple Model for Random Forests

Jason Klusowski

Nonparametric Variable Screening with Optimal Decision Stumps

Jason Klusowski, Peter Tian

Sample efficient learning of image-based diagnostic classifiers via probabilistic labels

Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth

Scalable Constrained Bayesian Optimization

David Eriksson, Matthias Poloczek

Shapley Flow: A Graph-based Approach to Interpreting Model Predictions

Jiaxuan Wang, Jenna Wiens, Scott Lundberg

Generalized Spectral Clustering via Gromov-Wasserstein Learning

Samir Chowdhury, Tom Needham

Improving predictions of Bayesian neural nets via local linearization

Alexander Immer, Maciej Korzepa, Matthias Bauer

Predictive Complexity Priors

Eric Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato

Neural Enhanced Belief Propagation on Factor Graphs

Victor Garcia Satorras, Max Welling

Automatic structured variational inference

Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne

Logical Team Q-learning: An approach towards factored policies in cooperative MARL

Lucas Cassano, Ali H. Sayed

Self-Concordant Analysis of Generalized Linear Bandits with Forgetting

Yoan Russac, Louis Faury, Olivier Cappe, Aurélien Garivier

Kernel regression in high dimensions: Refined analysis beyond double descent

Fanghui Liu, Zhenyu Liao, Johan Suykens

Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting

Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári

Geometrically Enriched Latent Spaces

Georgios Arvanitidis, Soren Hauberg, Bernhard Schölkopf

Bandit algorithms: Letting go of logarithmic regret for statistical robustness

Kumar Ashutosh, Jayakrishnan Nair, Anmol Kagrecha, Krishna Jagannathan

CADA: Communication-Adaptive Distributed Adam

Tianyi Chen, Ziye Guo, Yuejiao Sun, Wotao Yin

On the Role of Data in PAC-Bayes Bounds

Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel Roy

Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems

Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He

A Deterministic Streaming Sketch for Ridge Regression

Benwei Shi, Jeff Phillips

Learning to Defend by Learning to Attack

Haoming Jiang, Zhehui Chen, Yuyang Shi, Bo Dai, Tuo Zhao

Generalization Bounds for Stochastic Saddle Point Problems

Junyu Zhang, Mingyi Hong, Mengdi Wang, Shuzhong Zhang

Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering

Danny Vainstein, Vaggos Chatziafratis, Gui Citovsky, Anand Rajagopalan, Mohammad Mahdian

Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates

Damien Scieur, Lewis Liu, Thomas Pumir, Nicolas Boumal

Foundations of Bayesian Learning from Synthetic Data

Harrison Wilde, Jack Jewson, Sebastian Vollmer, Chris Holmes

Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications

Guillaume Ausset, Stéphan Clémençon, François Portier

Adaptive Approximate Policy Iteration

Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori, Pooria Joulani, Csaba Szepesvári

Adversarially Robust Estimate and Risk Analysis in Linear Regression

Yue Xing, Ruizhi Zhang, Guang Cheng

On the Generalization Properties of Adversarial Training

Yue Xing, Qifan Song, Guang Cheng

Predictive Power of Nearest Neighbors Algorithm under Random Perturbation

Yue Xing, Qifan Song, Guang Cheng