Michael I. Jordan - researchr alias

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Viewing Publication 1 - 100 from 533

2024

Collaborative Heterogeneous Causal Inference Beyond Meta-analysisTianyu Guo 0004, Sai Praneeth Karimireddy, Michael I. Jordan. icml 2024: [doi]

Delegating Data Collection in Decentralized Machine LearningNivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab. aistats 2024: 478-486 [doi]

Privacy Can Arise Endogenously in an Economic System with Learning AgentsNivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan. forc 2024: [doi]

Classifier Calibration with ROC-Regularized Isotonic RegressionEugene Berta, Francis R. Bach, Michael I. Jordan. aistats 2024: 1972-1980 [doi]

A Primal-Dual Approach to Solving Variational Inequalities with General ConstraintsTatjana Chavdarova, Tong Yang, Matteo Pagliardini, Michael I. Jordan. iclr 2024: [doi]

Chatbot Arena: An Open Platform for Evaluating LLMs by Human PreferenceWei-Lin Chiang, Lianmin Zheng, Ying Sheng 0007, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang 0108, Michael I. Jordan, Joseph E. Gonzalez, Ion Stoica. icml 2024: [doi]

Incentive-Aware Recommender Systems in Two-Sided MarketsXiaowu Dai, Wenlu Xu, Yuan Qi 0001, Michael I. Jordan. tors, 2(4), December 2024. [doi]

Conformal Decision Theory: Safe Autonomous Decisions from Imperfect PredictionsJordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik. icra 2024: 11668-11675 [doi]

A Specialized Semismooth Newton Method for Kernel-Based Optimal TransportTianyi Lin, Marco Cuturi, Michael I. Jordan. aistats 2024: 145-153 [doi]

A Diffusion Process Perspective on Posterior Contraction Rates for ParametersWenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan. simods, 6(2):553-577, 2024. [doi]

Incentivized Learning in Principal-Agent Bandit GamesAntoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael I. Jordan, El Mahdi El Mhamdi, Alain Oliviero Durmus. icml 2024: [doi]

On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information AsymmetrySerena Lutong Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan. aistats 2024: 1522-1530 [doi]

Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHFBanghua Zhu, Michael I. Jordan, Jiantao Jiao. icml 2024: [doi]

2023

KDD-2023 Workshop on Decision Intelligence and Analytics for Online MarketplacesZhiwei (Tony) Qin, Rui Song, Jieping Ye, Hongtu Zhu, Michael I. Jordan. kdd 2023: 5878-5879 [doi]

Provably Efficient Reinforcement Learning with Linear Function ApproximationChi Jin 0001, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan. mor, 48(3):1496-1521, August 2023. [doi]

Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers?Han Zhong 0001, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan. jmlr, 24, 2023. [doi]

Skilful nowcasting of extreme precipitation with NowcastNetYuchen Zhang 0004, Mingsheng Long, KaiYuan Chen, Lanxiang Xing, Ronghua Jin, Michael I. Jordan, Jianmin Wang 0001. nature, 619(7970):526-532, 2023. [doi]

Recommendation Systems with Distribution-Free Reliability GuaranteesAnastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan. copa 2023: 175-193 [doi]

Cilantro: Performance-Aware Resource Allocation for General Objectives via Online FeedbackRomil Bhardwaj, Kirthevasan Kandasamy, Asim Biswal, Wenshuo Guo, Benjamin Hindman, Joseph Gonzalez 0001, Michael I. Jordan, Ion Stoica. osdi 2023: 623-643 [doi]

On Learning Necessary and Sufficient Causal GraphsHengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song 0006. nips 2023: [doi]

A General Framework for Sample-Efficient Function Approximation in Reinforcement LearningZixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan. iclr 2023: [doi]

Class-Conditional Conformal Prediction with Many ClassesTiffany Ding, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani. nips 2023: [doi]

Neural Dependencies Emerging from Learning Massive CategoriesRuili Feng, Kecheng Zheng, Kai Zhu 0004, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael I. Jordan, Zheng-Jun Zha. cvpr 2023: 11711-11720 [doi]

A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective LearningNika Haghtalab, Michael I. Jordan, Eric Zhao 0003. nips 2023: [doi]

Modeling content creator incentives on algorithm-curated platformsJiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean. iclr 2023: [doi]

Competition, Alignment, and Equilibria in Digital MarketplacesMeena Jagadeesan, Michael I. Jordan, Nika Haghtalab. AAAI 2023: 5689-5696 [doi]

Improved Bayes Risk Can Yield Reduced Social Welfare Under CompetitionMeena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab. nips 2023: [doi]

Learning Equilibria in Matching Markets with Bandit FeedbackMeena Jagadeesan, Alexander Wei 0001, Yixin Wang, Michael I. Jordan, Jacob Steinhardt. JACM, 70(3), June 2023. [doi]

Deterministic Nonsmooth Nonconvex OptimizationMichael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis. colt 2023: 4570-4597 [doi]

First-Order Algorithms for Nonlinear Generalized Nash Equilibrium ProblemsMichael I. Jordan, Tianyi Lin, Manolis Zampetakis. jmlr, 24, 2023. [doi]

VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit FeedbackKirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica. jmlr, 24, 2023. [doi]

Nonconvex stochastic scaled gradient descent and generalized eigenvector problemsChris Junchi Li, Michael I. Jordan. uai 2023: 1230-1240 [doi]

Nesterov Meets Optimism: Rate-Optimal Separable Minimax OptimizationChris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan. icml 2023: 20351-20383 [doi]

A Statistical Analysis of Polyak-Ruppert Averaged Q-LearningXiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan. aistats 2023: 2207-2261 [doi]

Federated Conformal Predictors for Distributed Uncertainty QuantificationCharles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar. icml 2023: 22942-22964 [doi]

An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed BanditAldo Pacchiano, Peter L. Bartlett, Michael I. Jordan. alt 2023: 1166-1215 [doi]

On Learning Rates and Schrödinger OperatorsBin Shi, Weijie Su 0001, Michael I. Jordan. jmlr, 24, 2023. [doi]

Provably Personalized and Robust Federated LearningMariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy. tmlr, 2023, 2023. [doi]

Instance-Dependent Confidence and Early Stopping for Reinforcement LearningEric Xia, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan. jmlr, 24, 2023. [doi]

Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement LearningRuitu Xu, Yifei Min, Tianhao Wang 0002, Michael I. Jordan, Zhaoran Wang, Zhuoran Yang. aistats 2023: 375-407 [doi]

Solving Constrained Variational Inequalities via a First-order Interior Point-based MethodTong Yang, Michael I. Jordan, Tatjana Chavdarova. iclr 2023: [doi]

Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable StructureAngela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S. Du. nips 2023: [doi]

Online Learning in Stackelberg Games with an Omniscient FollowerGeng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan. icml 2023: 42304-42316 [doi]

Towards Optimal Caching and Model Selection for Large Model InferenceBanghua Zhu, Ying Sheng 0007, Lianmin Zheng, Clark W. Barrett, Michael I. Jordan, Jiantao Jiao. nips 2023: [doi]

Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise ComparisonsBanghua Zhu, Michael I. Jordan, Jiantao Jiao. icml 2023: 43037-43067 [doi]

The Sample Complexity of Online Contract DesignBanghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan. sigecom 2023: 1188 [doi]

Doubly-Robust Self-TrainingBanghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao. nips 2023: [doi]

Byzantine-Robust Federated Learning with Optimal Statistical RatesBanghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan. aistats 2023: 3151-3178 [doi]

2022

Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail and BeyondZhiwei (Tony) Qin, Liangjie Hong, Rui Song 0006, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan. kdd 2022: 4898-4899 [doi]

KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail, and BeyondZhiwei (Tony) Qin, Liangjie Hong, Rui Song 0006, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan. sigkdd, 24(2):78-80, 2022. [doi]

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in ImagingAnastasios N. Angelopoulos, Amit Pal Singh Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano. icml 2022: 717-730 [doi]

Optimal Mean Estimation without a VarianceYeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan. colt 2022: 356-357 [doi]

SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection ClassifierAdelson Chua, Michael I. Jordan, Rikky Muller. jssc, 57(8):2532-2544, 2022. [doi]

Rank Diminishing in Deep Neural NetworksRuili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael I. Jordan, Zheng-Jun Zha. nips 2022: [doi]

No-Regret Learning in Partially-Informed AuctionsWenshuo Guo, Michael I. Jordan, Ellen Vitercik. icml 2022: 8039-8055 [doi]

Off-Policy Evaluation with Policy-Dependent Optimization ResponseWenshuo Guo, Michael I. Jordan, Angela Zhou. nips 2022: [doi]

Learning Competitive Equilibria in Exchange Economies with Bandit FeedbackWenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez 0001, Michael I. Jordan, Ion Stoica. aistats 2022: 6200-6224 [doi]

Partial Identification with Noisy Covariates: A Robust Optimization ApproachWenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan. clear2 2022: 318-335 [doi]

Multi-Source Causal Inference Using Control Variates under Outcome Selection BiasWenshuo Guo, Serena Lutong Wang, Peng Ding, Yixin Wang, Michael I. Jordan. tmlr, 2022, 2022. [doi]

On-Demand Sampling: Learning Optimally from Multiple DistributionsNika Haghtalab, Michael I. Jordan, Eric Zhao 0003. nips 2022: [doi]

On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order AlgorithmsNhat Ho, Tianyi Lin, Michael I. Jordan. aistats 2022: 896-921 [doi]

Convergence Rates for Gaussian Mixtures of ExpertsNhat Ho, Chiao-Yu Yang, Michael I. Jordan. jmlr, 23, 2022. [doi]

Adaptivity of Stochastic Gradient Methods for Nonconvex OptimizationSamuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan. simods, 4(2):634-648, 2022. [doi]

Empirical Gateaux Derivatives for Causal InferenceMichael I. Jordan, Yixin Wang, Angela Zhou. nips 2022: [doi]

First-Order Algorithms for Min-Max Optimization in Geodesic Metric SpacesMichael I. Jordan, Tianyi Lin, Emmanouil-Vasileios Vlatakis-Gkaragkounis. nips 2022: [doi]

ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single AlgorithmChris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan. colt 2022: 909-981 [doi]

Learning Two-Player Markov Games: Neural Function Approximation and Correlated EquilibriumChris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan. nips 2022: [doi]

On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration AveragingChris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma 0001, Nicolas Le Roux, Michael I. Jordan. aistats 2022: 9793-9826 [doi]

Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex OptimizationTianyi Lin, Zeyu Zheng, Michael I. Jordan. nips 2022: [doi]

Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit FeedbackTianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan. icml 2022: 13441-13467 [doi]

A control-theoretic perspective on optimal high-order optimizationTianyi Lin, Michael I. Jordan. mp, 195(1):929-975, 2022. [doi]

On the Efficiency of Entropic Regularized Algorithms for Optimal TransportTianyi Lin, Nhat Ho, Michael I. Jordan. jmlr, 23, 2022. [doi]

On the Complexity of Approximating Multimarginal Optimal TransportTianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan. jmlr, 23, 2022. [doi]

Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange EconomyZhihan Liu, Miao Lu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang. icml 2022: 13870-13911 [doi]

Active Learning for Nonlinear System Identification with GuaranteesHoria Mania, Michael I. Jordan, Benjamin Recht. jmlr, 23, 2022. [doi]

Learn to Match with No Regret: Reinforcement Learning in Markov Matching MarketsYifei Min, Tianhao Wang 0002, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang. nips 2022: [doi]

On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical SystemsMichael Muehlebach, Michael I. Jordan. jmlr, 23, 2022. [doi]

Identifying Systematic Variation at the Single-Cell Level by Leveraging Low-Resolution Population-Level DataElior Rahmani, Michael I. Jordan, Nir Yosef. recomb 2022: 371 [doi]

Understanding the acceleration phenomenon via high-resolution differential equationsBin Shi, Simon S. Du, Michael I. Jordan, Weijie J. Su. mp, 195(1):79-148, 2022. [doi]

Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement LearningJibang Wu, Zixuan Zhang, Zhe Feng 0004, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu. sigecom 2022: 471-472 [doi]

Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model HubsKaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang 0001, Michael I. Jordan, Mingsheng Long. jmlr, 23, 2022. [doi]

Fast Distributionally Robust Learning with Variance-Reduced Min-Max OptimizationYaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan. aistats 2022: 1219-1250 [doi]

Robust Calibration with Multi-domain Temperature ScalingYaodong Yu, Stephen Bates, Yi Ma 0001, Michael I. Jordan. nips 2022: [doi]

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent KernelsYaodong Yu, Alexander Wei 0001, Sai Praneeth Karimireddy, Yi Ma 0001, Michael I. Jordan. nips 2022: [doi]

The 5th Artificial Intelligence of Things (AIoT) WorkshopJian Zhang, Jian Tang, Yiran Chen 0001, Jie Liu 0001, Jieping Ye, Marilyn Wolf, Vijaykrishnan Narayanan, Mani B. Srivastava, Michael I. Jordan, Victor Bahl. kdd 2022: 4912-4913 [doi]

Robust Estimation for Non-parametric Families via Generative Adversarial NetworksBanghua Zhu, Jiantao Jiao, Michael I. Jordan. isit 2022: 1100-1105 [doi]

2021

On the Theory of Reinforcement Learning with Once-per-Episode FeedbackNiladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan. nips 2021: 3401-3412 [doi]

Learning in Multi-Stage Decentralized Matching MarketsXiaowu Dai, Michael I. Jordan. nips 2021: 12798-12809 [doi]

Efficient Methods for Structured Nonconvex-Nonconcave Min-Max OptimizationJelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan. aistats 2021: 2746-2754 [doi]

The Stereotyping Problem in Collaboratively Filtered Recommender SystemsWenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg. eaamo 2021: [doi]

Robust Learning of Optimal AuctionsWenshuo Guo, Michael I. Jordan, Emmanouil Zampetakis. nips 2021: 21273-21284 [doi]

On Component Interactions in Two-Stage Recommender SystemsJiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus. nips 2021: 2744-2757 [doi]

Variational refinement for importance sampling using the forward Kullback-Leibler divergenceGhassen Jerfel, Serena Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan. uai 2021: 1819-1829 [doi]

Learning from eXtreme Bandit FeedbackRomain Lopez, Inderjit S. Dhillon, Michael I. Jordan. AAAI 2021: 8732-8740 [doi]

Test-time Collective PredictionCelestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan. nips 2021: 13719-13731 [doi]

Tactical Optimism and Pessimism for Deep Reinforcement LearningTed Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan. nips 2021: 12849-12863 [doi]

Representation Matters: Assessing the Importance of Subgroup Allocations in Training DataEsther Rolf, Theodora T. Worledge, Benjamin Recht, Michael I. Jordan. icml 2021: 9040-9051 [doi]

Provable Meta-Learning of Linear RepresentationsNilesh Tripuraneni, Chi Jin, Michael I. Jordan. icml 2021: 10434-10443 [doi]

On the Stability of Nonlinear Receding Horizon Control: A Geometric PerspectiveTyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry. cdc 2021: 742-749 [doi]

Who Leads and Who Follows in Strategic Classification?Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan. nips 2021: 15257-15269 [doi]

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