Michael I. Jordan

ORCID

Publication Activity (10 Years)

Years Active: 1989-2024
Publications (10 Years): 496

Top Venues

Publications

Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus
Unravelling in Collaborative Learning. CoRR (2024)
Alireza Fallah, Michael I. Jordan, Annie Ulichney
Fair Allocation in Dynamic Mechanism Design. CoRR (2024)
Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
Defection-Free Collaboration between Competitors in a Learning System. CoRR (2024)
Ezinne Nwankwo, Michael I. Jordan, Angela Zhou
Reduced-Rank Multi-objective Policy Learning and Optimization. CoRR (2024)
Tianyi Lin, Marco Cuturi, Michael I. Jordan
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. AISTATS (2024)
Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan
Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction. CoRR (2024)
Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart Russell
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. CoRR (2024)
Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions. ICRA (2024)
Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
Privacy Can Arise Endogenously in an Economic System with Learning Agents. FORC (2024)
Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian
The Limits of Price Discrimination Under Privacy Constraints. CoRR (2024)
Serena Lutong Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan
On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry. AISTATS (2024)
Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik, Michael I. Jordan
AutoEval Done Right: Using Synthetic Data for Model Evaluation. CoRR (2024)
Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. CoRR (2024)
Banghua Zhu, Michael I. Jordan, Jiantao Jiao
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. CoRR (2024)
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael I. Jordan, Joseph E. Gonzalez, Ion Stoica
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. CoRR (2024)
Tatjana Chavdarova, Tong Yang, Matteo Pagliardini, Michael I. Jordan
A Primal-Dual Approach to Solving Variational Inequalities with General Constraints. ICLR (2024)
Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR (2024)
Yi Zeng, Xuelin Yang, Li Chen, Cristian Canton Ferrer, Ming Jin, Michael I. Jordan, Ruoxi Jia
Fairness-Aware Meta-Learning via Nash Bargaining. CoRR (2024)
Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab
Delegating Data Collection in Decentralized Machine Learning. AISTATS (2024)
Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
Privacy Can Arise Endogenously in an Economic System with Learning Agents. CoRR (2024)
Vincent Blot, Anastasios N. Angelopoulos, Michael I. Jordan, Nicolas J.-B. Brunel
Automatically Adaptive Conformal Risk Control. CoRR (2024)
Serena Lutong Wang, Michael I. Jordan, Katrina Ligett, R. Preston McAfee
Information Elicitation in Agency Games. CoRR (2024)
Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus
Incentivized Learning in Principal-Agent Bandit Games. CoRR (2024)
Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. SIAM J. Math. Data Sci. 6 (2) (2024)
Eugene Berta, Francis R. Bach, Michael I. Jordan
Classifier Calibration with ROC-Regularized Isotonic Regression. AISTATS (2024)
Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian
On Three-Layer Data Markets. CoRR (2024)
Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus
Mitigating Externalities while Learning: an Online Version of the Coase Theorem. CoRR (2024)
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
Federated Conformal Predictors for Distributed Uncertainty Quantification. CoRR (2023)
Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt
Incentivizing High-Quality Content in Online Recommender Systems. CoRR (2023)
Nika Haghtalab, Michael I. Jordan, Eric Zhao
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning. NeurIPS (2023)
Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. ICML (2023)
Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback. J. Mach. Learn. Res. 24 (2023)
Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions. CoRR (2023)
Michael I. Jordan, Tianyi Lin, Manolis Zampetakis
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. J. Mach. Learn. Res. 24 (2023)
Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan
Byzantine-Robust Federated Learning with Optimal Statistical Rates. AISTATS (2023)
Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
Provably Efficient Reinforcement Learning with Linear Function Approximation. Math. Oper. Res. 48 (3) (2023)
Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan
A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. AISTATS (2023)
Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S. Du
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure. NeurIPS (2023)
Banghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao
Doubly Robust Self-Training. CoRR (2023)
Yuchen Zhang, Mingsheng Long, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael I. Jordan, Jianmin Wang
Skilful nowcasting of extreme precipitation with NowcastNet. Nat. 619 (7970) (2023)
Michael I. Jordan, Tianyi Lin, Zhengyuan Zhou
Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback. CoRR (2023)
Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi
Provably Personalized and Robust Federated Learning. CoRR (2023)
Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. ICLR (2023)
Banghua Zhu, Hiteshi Sharma, Felipe Vieira Frujeri, Shi Dong, Chenguang Zhu, Michael I. Jordan, Jiantao Jiao
Fine-Tuning Language Models with Advantage-Induced Policy Alignment. CoRR (2023)
Banghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao
Doubly-Robust Self-Training. NeurIPS (2023)
Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
Scaff-PD: Communication Efficient Fair and Robust Federated Learning. CoRR (2023)
Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan
Online Learning in Stackelberg Games with an Omniscient Follower. ICML (2023)
Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis
Deterministic Nonsmooth Nonconvex Optimization. CoRR (2023)
Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan
Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR (2023)
Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark W. Barrett, Michael I. Jordan, Jiantao Jiao
On Optimal Caching and Model Multiplexing for Large Model Inference. CoRR (2023)
Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani
Class-Conditional Conformal Prediction With Many Classes. CoRR (2023)
Tiffany Ding, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani
Class-Conditional Conformal Prediction with Many Classes. NeurIPS (2023)
Alireza Fallah, Michael I. Jordan
Contract Design With Safety Inspections. CoRR (2023)
Francisca Vasconcelos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos, Georgios Piliouras, Michael I. Jordan
A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games. CoRR (2023)
Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab
Delegating Data Collection in Decentralized Machine Learning. CoRR (2023)
Chris Junchi Li, Michael I. Jordan
Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. UAI (2023)
Romil Bhardwaj, Kirthevasan Kandasamy, Asim Biswal, Wenshuo Guo, Benjamin Hindman, Joseph Gonzalez, Michael I. Jordan, Ion Stoica
Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback. OSDI (2023)
Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab
Competition, Alignment, and Equilibria in Digital Marketplaces. AAAI (2023)
Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael I. Jordan, Zheng-Jun Zha
Neural Dependencies Emerging from Learning Massive Categories. CVPR (2023)
Ruitu Xu, Yifei Min, Tianhao Wang, Michael I. Jordan, Zhaoran Wang, Zhuoran Yang
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning. AISTATS (2023)
Neha S. Wadia, Yatin Dandi, Michael I. Jordan
A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning. CoRR (2023)
Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff
Incentive-Theoretic Bayesian Inference for Collaborative Science. CoRR (2023)
Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT (2023)
Michael Muehlebach, Michael I. Jordan
Accelerated First-Order Optimization under Nonlinear Constraints. CoRR (2023)
Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan
Recommendation Systems with Distribution-Free Reliability Guarantees. COPA (2023)
Tong Yang, Michael I. Jordan, Tatjana Chavdarova
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method. ICLR (2023)
Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song
On Learning Necessary and Sufficient Causal Graphs. NeurIPS (2023)
Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? J. Mach. Learn. Res. 24 (2023)
Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song
On Learning Necessary and Sufficient Causal Graphs. CoRR (2023)
Ruitu Xu, Yifei Min, Tianhao Wang, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning. CoRR (2023)
Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis
Deterministic Nonsmooth Nonconvex Optimization. COLT (2023)
Serena Lutong Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry. CoRR (2023)
Mariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy
Provably Personalized and Robust Federated Learning. Trans. Mach. Learn. Res. 2023 (2023)
Tianyi Lin, Marco Cuturi, Michael I. Jordan
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. CoRR (2023)
Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. CoRR (2023)
Nika Haghtalab, Michael I. Jordan, Eric Zhao
A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning. CoRR (2023)
Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan
The Sample Complexity of Online Contract Design. EC (2023)
Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark W. Barrett, Michael I. Jordan, Jiantao Jiao
Towards Optimal Caching and Model Selection for Large Model Inference. NeurIPS (2023)
Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. CoRR (2023)
Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt
Learning Equilibria in Matching Markets with Bandit Feedback. J. ACM 70 (3) (2023)
Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Michael I. Jordan
Towards Optimal Statistical Watermarking. CoRR (2023)
Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean
Modeling content creator incentives on algorithm-curated platforms. ICLR (2023)
Banghua Zhu, Jiantao Jiao, Michael I. Jordan
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. CoRR (2023)
Yang Cai, Michael I. Jordan, Tianyi Lin, Argyris Oikonomou, Emmanouil-Vasileios Vlatakis-Gkaragkounis
Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds. CoRR (2023)
Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan
Online Learning in a Creator Economy. CoRR (2023)
Banghua Zhu, Michael I. Jordan, Jiantao Jiao
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. ICML (2023)
Zhiwei (Tony) Qin, Rui Song, Jieping Ye, Hongtu Zhu, Michael I. Jordan
KDD-2023 Workshop on Decision Intelligence and Analytics for Online Marketplaces. KDD (2023)
Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic
Prediction-Powered Inference. CoRR (2023)
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
Federated Conformal Predictors for Distributed Uncertainty Quantification. ICML (2023)
Eugene Berta, Francis R. Bach, Michael I. Jordan
Classifier Calibration with ROC-Regularized Isotonic Regression. CoRR (2023)
Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. NeurIPS (2023)
Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan
Online Learning in Stackelberg Games with an Omniscient Follower. CoRR (2023)
Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan
On the Complexity of Approximating Multimarginal Optimal Transport. J. Mach. Learn. Res. 23 (2022)
Banghua Zhu, Jiantao Jiao, Michael I. Jordan
Robust Estimation for Non-parametric Families via Generative Adversarial Networks. ISIT (2022)
Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan
Mechanisms that Incentivize Data Sharing in Federated Learning. CoRR (2022)
Elynn Y. Chen, Michael I. Jordan, Sai Li
Transferred Q-learning. CoRR (2022)
Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design. CoRR (2022)
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 G. Parameswaran, David A. Patterson, Raluca Ada Popa, Koushik Sen, Scott Shenker, Dawn Song, Ion Stoica
The Sky Above The Clouds. CoRR (2022)
Wenshuo Guo, Serena Lutong Wang, Peng Ding, Yixin Wang, Michael I. Jordan
Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias. Trans. Mach. Learn. Res. 2022 (2022)
Alessandro Barp, Lancelot Da Costa, Guilherme França, Karl J. Friston, Mark A. Girolami, Michael I. Jordan, Grigorios A. Pavliotis
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents. CoRR (2022)

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Publication Activity (10 Years)

Top Topics

Top Venues

Michael I. Jordan