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ORCIDMichael I. Jordan
Person information
- affiliation: University of California, Berkeley, Department of Electrical Engineering and Computer Science
- affiliation: University of California, Berkeley, Department of Statistics
- affiliation: Massachusetts Institute of Technology, Center for Biological and Computational Learning
- award (2009): ACM - AAAI Allen Newell Award
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2020 – today
- 2023
[j121]Meena Jagadeesan
, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt:
Learning Equilibria in Matching Markets with Bandit Feedback. J. ACM 70(3): 19:1-19:46 (2023)- [j120]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: 35:1-35:52 (2023) - [j119]Michael I. Jordan, Tianyi Lin, Manolis Zampetakis:
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. J. Mach. Learn. Res. 24: 38:1-38:46 (2023) - [j118]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: 53:1-53:45 (2023) - [j117]Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Provably Efficient Reinforcement Learning with Linear Function Approximation. Math. Oper. Res. 48(3): 1496-1521 (2023) - [c387]Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab:
Competition, Alignment, and Equilibria in Digital Marketplaces. AAAI 2023: 5689-5696 - [c386]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: 375-407 - [c385]Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan:
A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. AISTATS 2023: 2207-2261 - [c384]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: 3151-3178 - [c383]Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT 2023: 1166-1215 - [c382]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. COLT 2023: 4570-4597 - [c381]Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan:
Recommendation Systems with Distribution-Free Reliability Guarantees. COPA 2023: 175-193 - [c380]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: 11711-11720 - [c379]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 - [c378]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean:
Modeling content creator incentives on algorithm-curated platforms. ICLR 2023 - [c377]Tong Yang, Michael I. Jordan, Tatjana Chavdarova:
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method. ICLR 2023 - [c376]Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan:
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. ICML 2023: 20351-20383 - [c375]Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar:
Federated Conformal Predictors for Distributed Uncertainty Quantification. ICML 2023: 22942-22964 - [c374]Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Online Learning in Stackelberg Games with an Omniscient Follower. ICML 2023: 42304-42316 - [c373]Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. ICML 2023: 43037-43067 - [c372]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: 5878-5879 - [c371]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: 623-643 - [c370]Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan:
The Sample Complexity of Online Contract Design. EC 2023: 1188 - [c369]Chris Junchi Li, Michael I. Jordan:
Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. UAI 2023: 1230-1240 - [i321]Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic:
Prediction-Powered Inference. CoRR abs/2301.09633 (2023) - [i320]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. CoRR abs/2301.11270 (2023) - [i319]Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Online Learning in Stackelberg Games with an Omniscient Follower. CoRR abs/2301.11518 (2023) - [i318]Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song:
On Learning Necessary and Sufficient Causal Graphs. CoRR abs/2301.12389 (2023) - [i317]Michael Muehlebach, Michael I. Jordan:
Accelerated First-Order Optimization under Nonlinear Constraints. CoRR abs/2302.00316 (2023) - [i316]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. CoRR abs/2302.08300 (2023) - [i315]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning. CoRR abs/2302.10863 (2023) - [i314]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 abs/2303.04833 (2023) - [i313]Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan:
Online Learning in a Creator Economy. CoRR abs/2305.11381 (2023) - [i312]Serena Lutong Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan:
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry. CoRR abs/2305.14595 (2023) - [i311]Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar:
Federated Conformal Predictors for Distributed Uncertainty Quantification. CoRR abs/2305.17564 (2023) - [i310]Banghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao:
Doubly Robust Self-Training. CoRR abs/2306.00265 (2023) - [i309]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 abs/2306.02003 (2023) - [i308]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 abs/2306.02231 (2023) - [i307]Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan:
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. CoRR abs/2306.05592 (2023) - [i306]Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt:
Incentivizing High-Quality Content in Online Recommender Systems. CoRR abs/2306.07479 (2023) - [i305]Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi:
Provably Personalized and Robust Federated Learning. CoRR abs/2306.08393 (2023) - [i304]Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani:
Class-Conditional Conformal Prediction With Many Classes. CoRR abs/2306.09335 (2023) - [i303]Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab:
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. CoRR abs/2306.14670 (2023) - [i302]Yang Cai, Michael I. Jordan, Tianyi Lin, Argyris Oikonomou, Emmanouil-Vasileios Vlatakis-Gkaragkounis:
Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds. CoRR abs/2306.16617 (2023) - [i301]Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan:
Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR abs/2307.00126 (2023) - [i300]Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff:
Incentive-Theoretic Bayesian Inference for Collaborative Science. CoRR abs/2307.03748 (2023) - [i299]Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
Scaff-PD: Communication Efficient Fair and Robust Federated Learning. CoRR abs/2307.13381 (2023) - [i298]Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab:
Delegating Data Collection in Decentralized Machine Learning. CoRR abs/2309.01837 (2023) - [i297]Neha S. Wadia, Yatin Dandi, Michael I. Jordan:
A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning. CoRR abs/2309.04877 (2023) - [i296]Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik:
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions. CoRR abs/2310.05921 (2023) - [i295]Michael I. Jordan, Tianyi Lin, Zhengyuan Zhou:
Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback. CoRR abs/2310.14085 (2023) - [i294]Tianyi Lin, Marco Cuturi, Michael I. Jordan:
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. CoRR abs/2310.14087 (2023) - [i293]Alireza Fallah, Michael I. Jordan:
Contract Design With Safety Inspections. CoRR abs/2311.02537 (2023) - [i292]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 abs/2311.10859 (2023) - [i291]Eugene Berta, Francis R. Bach, Michael I. Jordan:
Classifier Calibration with ROC-Regularized Isotonic Regression. CoRR abs/2311.12436 (2023) - 2022
- [j116]Horia Mania, Michael I. Jordan, Benjamin Recht:
Active Learning for Nonlinear System Identification with Guarantees. J. Mach. Learn. Res. 23: 32:1-32:30 (2022) - [j115]Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan:
On the Complexity of Approximating Multimarginal Optimal Transport. J. Mach. Learn. Res. 23: 65:1-65:43 (2022) - [j114]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On the Efficiency of Entropic Regularized Algorithms for Optimal Transport. J. Mach. Learn. Res. 23: 137:1-137:42 (2022) - [j113]Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long:
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs. J. Mach. Learn. Res. 23: 209:1-209:47 (2022) - [j112]Michael Muehlebach, Michael I. Jordan:
On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems. J. Mach. Learn. Res. 23: 256:1-256:47 (2022) - [j111]Nhat Ho, Chiao-Yu Yang, Michael I. Jordan:
Convergence Rates for Gaussian Mixtures of Experts. J. Mach. Learn. Res. 23: 323:1-323:81 (2022) - [j110]Adelson Chua, Michael I. Jordan, Rikky Muller:
SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier. IEEE J. Solid State Circuits 57(8): 2532-2544 (2022) - [j109]Bin Shi, Simon S. Du, Michael I. Jordan, Weijie J. Su:
Understanding the acceleration phenomenon via high-resolution differential equations. Math. Program. 195(1): 79-148 (2022) - [j108]Tianyi Lin, Michael I. Jordan:
A control-theoretic perspective on optimal high-order optimization. Math. Program. 195(1): 929-975 (2022) - [j107]Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan:
KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail, and Beyond. SIGKDD Explor. 24(2): 78-80 (2022) - [j106]Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan:
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. SIAM J. Math. Data Sci. 4(2): 634-648 (2022) - [j105]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) - [c368]Nhat Ho, Tianyi Lin, Michael I. Jordan:
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. AISTATS 2022: 896-921 - [c367]Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS 2022: 1219-1250 - [c366]Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica:
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. AISTATS 2022: 6200-6224 - [c365]Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan:
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. AISTATS 2022: 9793-9826 - [c364]Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan:
Partial Identification with Noisy Covariates: A Robust Optimization Approach. CLeaR 2022: 318-335 - [c363]Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan:
Optimal Mean Estimation without a Variance. COLT 2022: 356-357 - [c362]Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan:
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. COLT 2022: 909-981 - [c361]Anastasios N. Angelopoulos, Amit Pal Singh Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano:
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. ICML 2022: 717-730 - [c360]Wenshuo Guo, Michael I. Jordan, Ellen Vitercik:
No-Regret Learning in Partially-Informed Auctions. ICML 2022: 8039-8055 - [c359]Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. ICML 2022: 13441-13467 - [c358]Zhihan Liu, Miao Lu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy. ICML 2022: 13870-13911 - [c357]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Robust Estimation for Non-parametric Families via Generative Adversarial Networks. ISIT 2022: 1100-1105 - [c356]Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan:
Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail and Beyond. KDD 2022: 4898-4899 - [c355]Jian Zhang, Jian Tang, Yiran Chen, Jie Liu, Jieping Ye, Marilyn Wolf, Vijaykrishnan Narayanan, Mani B. Srivastava, Michael I. Jordan, Victor Bahl:
The 5th Artificial Intelligence of Things (AIoT) Workshop. KDD 2022: 4912-4913 - [c354]Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael I. Jordan, Zheng-Jun Zha:
Rank Diminishing in Deep Neural Networks. NeurIPS 2022 - [c353]Wenshuo Guo, Michael I. Jordan, Angela Zhou:
Off-Policy Evaluation with Policy-Dependent Optimization Response. NeurIPS 2022 - [c352]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
On-Demand Sampling: Learning Optimally from Multiple Distributions. NeurIPS 2022 - [c351]Michael I. Jordan, Tianyi Lin, Emmanouil-Vasileios Vlatakis-Gkaragkounis:
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. NeurIPS 2022 - [c350]Michael I. Jordan, Yixin Wang, Angela Zhou:
Empirical Gateaux Derivatives for Causal Inference. NeurIPS 2022 - [c349]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS 2022 - [c348]Tianyi Lin, Zeyu Zheng, Michael I. Jordan:
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. NeurIPS 2022 - [c347]Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. NeurIPS 2022 - [c346]Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. NeurIPS 2022 - [c345]Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan:
Robust Calibration with Multi-domain Temperature Scaling. NeurIPS 2022 - [c344]Elior Rahmani, Michael I. Jordan, Nir Yosef:
Identifying Systematic Variation at the Single-Cell Level by Leveraging Low-Resolution Population-Level Data. RECOMB 2022: 371 - [c343]Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu:
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. EC 2022: 471-472 - [i290]Wenlong Mou, Koulik Khamaru, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
Optimal variance-reduced stochastic approximation in Banach spaces. CoRR abs/2201.08518 (2022) - [i289]Koulik Khamaru, Eric Xia, Martin J. Wainwright, Michael I. Jordan:
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning. CoRR abs/2201.08536 (2022) - [i288]Mariel A. Werner, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan:
Online Active Learning with Dynamic Marginal Gain Thresholding. CoRR abs/2201.10547 (2022) - [i287]Elynn Y. Chen, Rui Song, Michael I. Jordan:
Reinforcement Learning with Heterogeneous Data: Estimation and Inference. CoRR abs/2202.00088 (2022) - [i286]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Robust Estimation for Nonparametric Families via Generative Adversarial Networks. CoRR abs/2202.01269 (2022) - [i285]Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, Michael I. Jordan:
Conformal prediction for the design problem. CoRR abs/2202.03613 (2022) - [i284]Elynn Y. Chen, Michael I. Jordan, Sai Li:
Transferred Q-learning. CoRR abs/2202.04709 (2022) - [i283]Anastasios N. Angelopoulos, Amit P. S. Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano:
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. CoRR abs/2202.05265 (2022) - [i282]Matteo Pagliardini, Gilberto Manunza, Martin Jaggi, Michael I. Jordan, Tatjana Chavdarova:
Improving Generalization via Uncertainty Driven Perturbations. CoRR abs/2202.05737 (2022) - [i281]Wenshuo Guo, Michael I. Jordan, Ellen Vitercik:
No-Regret Learning in Partially-Informed Auctions. CoRR abs/2202.10606 (2022) - [i280]Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan:
Partial Identification with Noisy Covariates: A Robust Optimization Approach. CoRR abs/2202.10665 (2022) - [i279]Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu:
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. CoRR abs/2202.10678 (2022) - [i278]Boxiang Lyu, Qinglin Meng, Shuang Qiu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan:
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach. CoRR abs/2202.12797 (2022) - [i277]Wenshuo Guo, Michael I. Jordan, Angela Zhou:
Off-Policy Evaluation with Policy-Dependent Optimization Response. CoRR abs/2202.12958 (2022) - [i276]Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. CoRR abs/2203.03684 (2022) - [i275]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 abs/2203.10592 (2022) - [i274]Michael I. Jordan, Tianyi Lin, Manolis Zampetakis:
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. CoRR abs/2204.03132 (2022) - [i273]Tianyi Lin, Michael I. Jordan:
Perseus: A Simple High-Order Regularization Method for Variational Inequalities. CoRR abs/2205.03202 (2022) - [i272]Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff:
Principal-Agent Hypothesis Testing. CoRR abs/2205.06812 (2022) - [i271]Sarah E. 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 abs/2205.07147 (2022) - [i270]Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. CoRR abs/2205.07217 (2022) - [i269]Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan:
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees. CoRR abs/2205.11765 (2022) - [i268]Michael I. Jordan, Tianyi Lin, Emmanouil-Vasileios Vlatakis-Gkaragkounis:
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. CoRR abs/2206.02041 (2022) - [i267]Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan:
Robust Calibration with Multi-domain Temperature Scaling. CoRR abs/2206.02757 (2022) - [i266]Tianyi Lin, Michael I. Jordan:
A Continuous-Time Perspective on Monotone Equation Problems. CoRR abs/2206.04770 (2022) - [i265]Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael I. Jordan, Zheng-Jun Zha:
Rank Diminishing in Deep Neural Networks. CoRR abs/2206.06072 (2022) - [i264]Simon S. Du, Gauthier Gidel, Michael I. Jordan, Chris Junchi Li:
Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. CoRR abs/2206.08573 (2022) - [i263]