Michael I. Jordan

SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier

Adelson Chua, Michael I. Jordan, Rikky Muller

Comments Copyright 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Journal ref IEEE Journal of Solid-State Circuits, 2022

On the Efficiency of Entropic Regularized Algorithms for Optimal Transport

Tianyi Lin, Nhat Ho, Michael I. Jordan

Comments Accepted by Journal of Machine Learning Research; A preliminary version [arXiv:1901.06482] of this paper, with a subset of the results that are presented here, was presented at ICML 2019; 39 pages, 21 figures

The Sky Above The Clouds

Sarah Chasins, Alvin Cheung, Natacha Crooks, Ali Ghodsi, Ken Goldberg, Joseph E. Gonzalez, Joseph M. Hellerstein, Michael I. Jordan, Anthony D. Joseph, Michael W. Mahoney, Aditya Parameswaran, David Patterson, Raluca Ada Popa, Koushik Sen, Scott Shenker, Dawn Song, Ion Stoica

Comments 35 pages

On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging

Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan

Comments Camera-ready version appeared at AISTATS 2022; short version appeared at NeurIPS OPT 2021 Workshop

Tactical Optimism and Pessimism for Deep Reinforcement Learning

Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan

ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning

Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, Michael I. Jordan

Comments 9 pages, 7 figures

Journal ref Deep Reinforcement Learning Workshop, NeurIPS 2021

Instability, Computational Efficiency and Statistical Accuracy

Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu

Comments 68 pages, 6 Figures, 2 Tables. First three authors contributed equally

Post-Selection Inference via Algorithmic Stability

Tijana Zrnic, Michael I. Jordan

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets

Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang

Comments 40 pages

Convergence Rates for Gaussian Mixtures of Experts

Nhat Ho, Chiao-Yu Yang, Michael I. Jordan

Comments 81 pages

Improving Generalization via Uncertainty Driven Perturbations

Matteo Pagliardini, Gilberto Manunza, Martin Jaggi, Michael I. Jordan, Tatjana Chavdarova

Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning

Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu

Partial Identification with Noisy Covariates: A Robust Optimization Approach

Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan

Comments Proceedings of Conference on Causal Learning and Reasoning (CLeaR) 2022

Learning Competitive Equilibria in Exchange Economies with Bandit Feedback

Wenshuo Guo, Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I. Jordan, Ion Stoica

Comments Proceedings of The 25nd International Conference on Artificial Intelligence and Statistics; 32 pages, 6 figures

On the Complexity of Approximating Multimarginal Optimal Transport

Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan

Comments Accepted by Journal of Machine Learning Research; Add the references and the funding information; 40 pages, 14 figures

Desiderata for Representation Learning: A Causal Perspective

Yixin Wang, Michael I. Jordan

Comments 68 pages

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

Anastasios N Angelopoulos, Amit P Kohli, Stephen Bates, Michael I Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano

Comments Code available at https://github.com/aangelopoulos/im2im-uq

Robust Estimation for Nonparametric Families via Generative Adversarial Networks

Banghua Zhu, Jiantao Jiao, Michael I. Jordan

Reinforcement Learning with Heterogeneous Data: Estimation and Inference

Elynn Y. Chen, Rui Song, Michael I. Jordan

Who Leads and Who Follows in Strategic Classification?

Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan

Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics

Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick

Comments 69 pages. Accepted for submission at Bayesian Analysis

Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization

Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan

Comments Accepted by AISTATS 2022; The first three authors contributed equally to this work; 43 pages, 28 figures

On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms

Nhat Ho, Tianyi Lin, Michael I. Jordan

Comments Accepted by AISTATS 2022; The first two authors contributed equally to this work; 36 Pages, 18 figures

Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems

Chris Junchi Li, Michael I. Jordan

Comments Minor typographical updates

VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback

Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica

Instance-Dependent Confidence and Early Stopping for Reinforcement Learning

Koulik Khamaru, Eric Xia, Martin J. Wainwright, Michael I. Jordan

On component interactions in two-stage recommender systems

Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus

Comments Appears in the proceedings of the NeurIPS 2021 conference

Provable Meta-Learning of Linear Representations

Nilesh Tripuraneni, Chi Jin, Michael I. Jordan

Comments Lower bound slightly improved to include task diversity parameter

Multi-Source Causal Inference Using Control Variates

Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, Michael I. Jordan

Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?

Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan