Jordan, Michael

98 publications

AISTATS 2025 Automatically Adaptive Conformal Risk Control Vincent Blot, Anastasios Nikolas Angelopoulos, Michael Jordan, Nicolas J-B. Brunel

AISTATS 2025 Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas Courtade, Alireza Fallah, Michael Jordan

ICLR 2024 A Primal-Dual Approach to Solving Variational Inequalities with General Constraints Tatjana Chavdarova, Tong Yang, Matteo Pagliardini, Michael Jordan

AISTATS 2024 A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport Tianyi Lin, Marco Cuturi, Michael Jordan

NeurIPSW 2024 Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs Tianyu Guo, Druv Pai, Yu Bai, Jiantao Jiao, Michael Jordan, Song Mei

ICML 2024 Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael Jordan, Joseph E. Gonzalez, Ion Stoica

AISTATS 2024 Classifier Calibration with ROC-Regularized Isotonic Regression Eugène Berta, Francis Bach, Michael Jordan

ICML 2024 Collaborative Heterogeneous Causal Inference Beyond Meta-Analysis Tianyu Guo, Sai Praneeth Karimireddy, Michael Jordan

NeurIPSW 2024 Defection-Free Collaboration Between Competitors in a Learning System Mariel Werner, Sai Praneeth Karimireddy, Michael Jordan

AISTATS 2024 Delegating Data Collection in Decentralized Machine Learning Nivasini Ananthakrishnan, Stephen Bates, Michael Jordan, Nika Haghtalab

ICML 2024 Incentivized Learning in Principal-Agent Bandit Games Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus

ICML 2024 Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF Banghua Zhu, Michael Jordan, Jiantao Jiao

AISTATS 2024 On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry Serena Wang, Stephen Bates, P Aronow, Michael Jordan

ICMLW 2024 On Three-Layer Data Markets Alireza Fallah, Michael Jordan, Ali Makhdoumi, Azarakhsh Malekian

NeurIPS 2024 Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael Jordan, Jiantao Jiao, Yuandong Tian, Stuart Russell

ICLR 2023 A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael Jordan

NeurIPSW 2023 Accelerating Inexact HyperGradient Descent for Bilevel Optimization Haikuo Yang, Luo Luo, Chris Junchi Li, Michael Jordan, Maryam Fazel

ALT 2023 An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit Aldo Pacchiano, Peter Bartlett, Michael Jordan

COLT 2023 Deterministic Nonsmooth Nonconvex Optimization Michael Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis

ICMLW 2023 Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning Baihe Huang, Sai Praneeth Karimireddy, Michael Jordan

ICML 2023 Federated Conformal Predictors for Distributed Uncertainty Quantification Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael Jordan, Ramesh Raskar

ICMLW 2023 Federated Conformal Predictors for Distributed Uncertainty Quantification Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael Jordan, Ramesh Raskar

NeurIPSW 2023 Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data Nilesh Tripuraneni, Dominique Perrault-Joncas, Dhruv Madeka, Dean Foster, Michael Jordan

ICLR 2023 Modeling Content Creator Incentives on Algorithm-Curated Platforms Jiri Hron, Karl Krauth, Michael Jordan, Niki Kilbertus, Sarah Dean

ICML 2023 Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael Jordan

CVPR 2023 Neural Dependencies Emerging from Learning Massive Categories Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael Jordan, Zheng-Jun Zha

ICML 2023 Online Learning in Stackelberg Games with an Omniscient Follower Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael Jordan

ICLRW 2023 Principled Reinforcement Learning with Human Feedback from Pairwise or $k$-Wise Comparisons Banghua Zhu, Jiantao Jiao, Michael Jordan

ICMLW 2023 Principled Reinforcement Learning with Human Feedback from Pairwise or $k$-Wise Comparisons Banghua Zhu, Michael Jordan, Jiantao Jiao

ICML 2023 Principled Reinforcement Learning with Human Feedback from Pairwise or K-Wise Comparisons Banghua Zhu, Michael Jordan, Jiantao Jiao

TMLR 2023 Provably Personalized and Robust Federated Learning Mariel Werner, Lie He, Michael Jordan, Martin Jaggi, Sai Praneeth Karimireddy

ICMLW 2023 SCAFF-PD: Communication Efficient Fair and Robust Federated Learning Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael Jordan

ICLR 2023 Solving Constrained Variational Inequalities via a First-Order Interior Point-Based Method Tong Yang, Michael Jordan, Tatjana Chavdarova

NeurIPSW 2023 Towards Optimal Statistical Watermarking Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason Lee, Jiantao Jiao, Michael Jordan

AISTATS 2022 Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael Jordan

AISTATS 2022 Learning Competitive Equilibria in Exchange Economies with Bandit Feedback Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael Jordan, Ion Stoica

AISTATS 2022 On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms Nhat Ho, Tianyi Lin, Michael Jordan

AISTATS 2022 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 Jordan

NeurIPSW 2022 A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael Jordan

ICLRW 2022 Can Reinforcement Learning Efficiently Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers？ Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael Jordan

ICML 2022 Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging Anastasios N Angelopoulos, Amit Pal Kohli, Stephen Bates, Michael Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano

NeurIPSW 2022 Mechanisms That Incentivize Data Sharing in Federated Learning Sai Praneeth Karimireddy, Wenshuo Guo, Michael Jordan

TMLR 2022 Multi-Source Causal Inference Using Control Variates Under Outcome Selection Bias Wenshuo Guo, Serena Lutong Wang, Peng Ding, Yixin Wang, Michael Jordan

NeurIPSW 2022 Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization Chris Junchi Li, Angela Yuan, Gauthier Gidel, Michael Jordan

ICML 2022 No-Regret Learning in Partially-Informed Auctions Wenshuo Guo, Michael Jordan, Ellen Vitercik

ICML 2022 Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael Jordan

COLT 2022 Optimal Mean Estimation Without a Variance Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter Bartlett, Michael Jordan

CLeaR 2022 Partial Identification with Noisy Covariates: A Robust Optimization Approach Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael Jordan

COLT 2022 ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm Chris Junchi Li, Wenlong Mou, Martin Wainwright, Michael Jordan

ICMLW 2022 Representation Learning as Finding Necessary and Sufficient Causes Yixin Wang, Michael Jordan

ICMLW 2022 Robust Calibration with Multi-Domain Temperature Scaling Yaodong Yu, Stephen Bates, Yi Ma, Michael Jordan

NeurIPSW 2022 Solving Constrained Variational Inequalities via a First-Order Interior Point-Based Method Tong Yang, Michael Jordan, Tatjana Chavdarova

NeurIPSW 2022 Towards Provably Personalized Federated Learning via Threshold-Clustering of Similar Clients Mariel Werner, Lie He, Sai Praneeth Karimireddy, Michael Jordan, Martin Jaggi

NeurIPSW 2022 Valid Inference After Causal Discovery Paula Gradu, Tijana Zrnic, Yixin Wang, Michael Jordan

ICML 2022 Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy Zhihan Liu, Miao Lu, Zhaoran Wang, Michael Jordan, Zhuoran Yang

NeurIPSW 2021 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 Jordan

NeurIPSW 2021 Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael Jordan

ICML 2021 Provable Meta-Learning of Linear Representations Nilesh Tripuraneni, Chi Jin, Michael Jordan

ICML 2021 Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data Esther Rolf, Theodora T Worledge, Benjamin Recht, Michael Jordan

ICML 2021 Resource Allocation in Multi-Armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael Jordan, Ken Goldberg, Joseph Gonzalez

COLT 2021 Stochastic Approximation for Online Tensorial Independent Component Analysis Chris Junchi Li, Michael Jordan

ICLR 2021 Uncertainty Sets for Image Classifiers Using Conformal Prediction Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan, Jitendra Malik

ICML 2020 Accelerated Message Passing for Entropy-Regularized MAP Inference Jonathan Lee, Aldo Pacchiano, Peter Bartlett, Michael Jordan

AISTATS 2020 Competing Bandits in Matching Markets Lydia T. Liu, Horia Mania, Michael Jordan

ICML 2020 Continuous-Time Lower Bounds for Gradient-Based Algorithms Michael Muehlebach, Michael Jordan

AISTATS 2020 Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference Jonathan Lee, Aldo Pacchiano, Michael Jordan

AISTATS 2020 Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter Wenshuo Guo, Nhat Ho, Michael Jordan

ICML 2020 Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael Jordan

ICML 2020 Learning to Score Behaviors for Guided Policy Optimization Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael Jordan

ICML 2020 On Approximate Thompson Sampling with Langevin Algorithms Eric Mazumdar, Aldo Pacchiano, Yian Ma, Michael Jordan, Peter Bartlett

ICML 2020 On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems Tianyi Lin, Chi Jin, Michael Jordan

AISTATS 2020 Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin Wainwright, Michael Jordan, Bin Yu

ICML 2020 Stochastic Gradient and Langevin Processes Xiang Cheng, Dong Yin, Peter Bartlett, Michael Jordan

AISTATS 2020 The Power of Batching in Multiple Hypothesis Testing Tijana Zrnic, Daniel Jiang, Aaditya Ramdas, Michael Jordan

ICML 2020 What Is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? Chi Jin, Praneeth Netrapalli, Michael Jordan

ICML 2019 A Dynamical Systems Perspective on Nesterov Acceleration Michael Muehlebach, Michael Jordan

AISTATS 2019 A Swiss Army Infinitesimal Jackknife Ryan Giordano, William Stephenson, Runjing Liu, Michael Jordan, Tamara Broderick

ICML 2019 Bridging Theory and Algorithm for Domain Adaptation Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael Jordan

ICML 2019 On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms Tianyi Lin, Nhat Ho, Michael Jordan

AISTATS 2019 Probabilistic Multilevel Clustering via Composite Transportation Distance Nhat Ho, Viet Huynh, Dinh Phung, Michael Jordan

ICML 2019 Rao-Blackwellized Stochastic Gradients for Discrete Distributions Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael Jordan, Jon Mcauliffe

ICML 2019 Theoretically Principled Trade-Off Between Robustness and Accuracy Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, Michael Jordan

ICML 2019 Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation Kaichao You, Ximei Wang, Mingsheng Long, Michael Jordan

ICML 2019 Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers Hong Liu, Mingsheng Long, Jianmin Wang, Michael Jordan

ICML 2018 Learning to Explain: An Information-Theoretic Perspective on Model Interpretation Jianbo Chen, Le Song, Martin Wainwright, Michael Jordan

ICML 2018 On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo Niladri Chatterji, Nicolas Flammarion, Yian Ma, Peter Bartlett, Michael Jordan

ICML 2018 RLlib: Abstractions for Distributed Reinforcement Learning Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, Ion Stoica

ICML 2018 SAFFRON: An Adaptive Algorithm for Online Control of the False Discovery Rate Aaditya Ramdas, Tijana Zrnic, Martin Wainwright, Michael Jordan

ICML 2016 A Kernelized Stein Discrepancy for Goodness-of-Fit Tests Qiang Liu, Jason Lee, Michael Jordan

ICML 2015 A General Analysis of the Convergence of ADMM Robert Nishihara, Laurent Lessard, Ben Recht, Andrew Packard, Michael Jordan

ICML 2015 Adding vs. Averaging in Distributed Primal-Dual Optimization Chenxin Ma, Virginia Smith, Martin Jaggi, Michael Jordan, Peter Richtarik, Martin Takac

ICML 2015 Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds Yuchen Zhang, Martin Wainwright, Michael Jordan

ICML 2015 Learning Transferable Features with Deep Adaptation Networks Mingsheng Long, Yue Cao, Jianmin Wang, Michael Jordan

ICML 2015 Trust Region Policy Optimization John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, Philipp Moritz

ICML 2013 Efficient Ranking from Pairwise Comparisons Fabian Wauthier, Michael Jordan, Nebojsa Jojic

ICML 2013 MAD-Bayes: MAP-Based Asymptotic Derivations from Bayes Tamara Broderick, Brian Kulis, Michael Jordan

AISTATS 2012 Stick-Breaking Beta Processes and the Poisson Process John Paisley, David Blei, Michael Jordan

AISTATS 2009 Coherence Functions for Multicategory Margin-Based Classification Methods Zhihua Zhang, Michael Jordan, Wu-Jun Li, Dit-Yan Yeung