Michael I. Jordan

Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry

Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt

Valid Inference After Causal Discovery

Paula Gradu, Tijana Zrnic, Yixin Wang, Michael I. Jordan

A Primal-Dual Approach to Solving Variational Inequalities with General Constraints

Tatjana Chavdarova, Tong Yang, Matteo Pagliardini, Michael I. Jordan

Comments Source code at https://github.com/Chavdarova/I-ACVI

Journal ref ICLR 2024

A Continuous-Time Perspective on Global Acceleration for Monotone Equation Problems

Tianyi Lin, Michael. I. Jordan

Comments Accepted by Communications in Optimization Theory; 29 Pages; Fix the inaccurate statement in Remark 2.7 and some typos

Defection-Free Collaboration between Competitors in a Learning System

Mariel Werner, Sai Praneeth Karimireddy, Michael I. Jordan

Incentivizing High-Quality Content in Online Recommender Systems

Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt

Comments Updated version with revised and expanded content

Incentive-Aware Recommender Systems in Two-Sided Markets

Xiaowu Dai, Wenlu Xu, Yuan Qi, Michael I. Jordan

The Limits of Price Discrimination Under Privacy Constraints

Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian

Fairness-Aware Meta-Learning via Nash Bargaining

Yi Zeng, Xuelin Yang, Li Chen, Cristian Canton Ferrer, Ming Jin, Michael I. Jordan, Ruoxi Jia

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems

Tianyi Lin, Chi Jin, Michael I. Jordan

Comments Accepted by ICML 2020; 39 pages, 6 figures

Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions

Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik

Comments 8 pages, 5 figures

Reduced-Rank Multi-objective Policy Learning and Optimization

Ezinne Nwankwo, Michael I. Jordan, Angela Zhou

Collaborative Heterogeneous Causal Inference Beyond Meta-analysis

Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan

Comments submitted to ICML

Privacy Can Arise Endogenously in an Economic System with Learning Agents

Nivasini Ananthakrishnan, Tiffany Ding, Mariel Werner, Sai Praneeth Karimireddy, Michael I. Jordan

Comments To appear in Symposium on Foundations of Responsible Computing (FORC 2024)

Principal-Agent Hypothesis Testing

Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff

On-Demand Sampling: Learning Optimally from Multiple Distributions

Nika Haghtalab, Michael I. Jordan, Eric Zhao

Comments 28 pages, 1 figure. Authors are ordered alphabetically. Outstanding paper award at the Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022). Version v2 updates a minor mistake in Lemma 3.1

Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic

Yufeng Zhang, Siyu Chen, Zhuoran Yang, Michael I. Jordan, Zhaoran Wang

Comments 41 pages, accepted to NeurIPS 2021, add acknowledgement

Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback

Michael I. Jordan, Tianyi Lin, Zhengyuan Zhou

Comments Accepted by Operations Research; 47 pages

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction

Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan

Comments 27 pages, 10 figures

Incentivized Learning in Principal-Agent Bandit Games

Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus

Private Prediction Sets

Anastasios N. Angelopoulos, Stephen Bates, Tijana Zrnic, Michael I. Jordan

Comments Code available at https://github.com/aangelopoulos/private_prediction_sets

Journal ref Harvard Data Science Review, 4(2). 2022

A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning

Neha S. Wadia, Yatin Dandi, Michael I. Jordan

Comments 36 pages, 7 figures; minor corrections

Perseus: A Simple and Optimal High-Order Method for Variational Inequalities

Tianyi Lin, Michael. I. Jordan

Comments Accepted by Mathematical Programming Series A; 40 pages

Incentive-Theoretic Bayesian Inference for Collaborative Science

Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff

Principled Reinforcement Learning with Human Feedback from Pairwise or $K$-wise Comparisons

Banghua Zhu, Jiantao Jiao, Michael I. Jordan

Towards Optimal Statistical Watermarking

Baihe Huang, Hanlin Zhu, Banghua Zhu, Kannan Ramchandran, Michael I. Jordan, Jason D. Lee, Jiantao Jiao

Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition

Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab

Comments Appeared at NeurIPS 2023; this is the full version

A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport

Tianyi Lin, Marco Cuturi, Michael I. Jordan

Comments Accepted by AISTATS 2024; Fix some inaccuracy in the definition and proof; 24 pages, 36 figures

Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF

Banghua Zhu, Michael I. Jordan, Jiantao Jiao

On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective

Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry