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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
- 2024
[j127]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): 553-577 (2024)- [j126]Xiaowu Dai, Wenlu Xu, Yuan Qi, Michael I. Jordan:
Incentive-Aware Recommender Systems in Two-Sided Markets. Trans. Recomm. Syst. 2(4): 32:1-32:38 (2024) - [c408]Tianyi Lin, Marco Cuturi, Michael I. Jordan:
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. AISTATS 2024: 145-153 - [c407]Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab:
Delegating Data Collection in Decentralized Machine Learning. AISTATS 2024: 478-486 - [c406]Serena Lutong Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan:
On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry. AISTATS 2024: 1522-1530 - [c405]Eugene Berta, Francis R. Bach, Michael I. Jordan:
Classifier Calibration with ROC-Regularized Isotonic Regression. AISTATS 2024: 1972-1980 - [c404]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: 9:1-9:22 - [c403]Tatjana Chavdarova, Tong Yang, Matteo Pagliardini, Michael I. Jordan:
A Primal-Dual Approach to Solving Variational Inequalities with General Constraints. ICLR 2024 - [c402]Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan:
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. ICML 2024 - [c401]Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael I. Jordan, Joseph E. Gonzalez, Ion Stoica:
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. ICML 2024 - [c400]Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus:
Incentivized Learning in Principal-Agent Bandit Games. ICML 2024 - [c399]Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. ICML 2024 - [c398]Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik:
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions. ICRA 2024: 11668-11675 - [i342]Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. CoRR abs/2401.16335 (2024) - [i341]Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian:
The Limits of Price Discrimination Under Privacy Constraints. CoRR abs/2402.08223 (2024) - [i340]Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian:
On Three-Layer Data Markets. CoRR abs/2402.09697 (2024) - [i339]Serena Lutong Wang, Michael I. Jordan, Katrina Ligett, R. Preston McAfee:
Information Elicitation in Agency Games. CoRR abs/2402.14005 (2024) - [i338]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 abs/2403.03811 (2024) - [i337]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 abs/2403.04132 (2024) - [i336]Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik, Michael I. Jordan:
AutoEval Done Right: Using Synthetic Data for Model Evaluation. CoRR abs/2403.07008 (2024) - [i335]Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar:
Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR abs/2403.13893 (2024) - [i334]Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan:
Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction. CoRR abs/2403.19605 (2024) - [i333]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 abs/2404.10767 (2024) - [i332]Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan:
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. CoRR abs/2404.15746 (2024) - [i331]Ezinne Nwankwo, Michael I. Jordan, Angela Zhou:
Reduced-Rank Multi-objective Policy Learning and Optimization. CoRR abs/2404.18490 (2024) - [i330]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 abs/2405.04669 (2024) - [i329]Alireza Fallah, Michael I. Jordan, Annie Ulichney:
Fair Allocation in Dynamic Mechanism Design. CoRR abs/2406.00147 (2024) - [i328]Yi Zeng, Xuelin Yang, Li Chen, Cristian Canton Ferrer, Ming Jin, Michael I. Jordan, Ruoxi Jia:
Fairness-Aware Meta-Learning via Nash Bargaining. CoRR abs/2406.07029 (2024) - [i327]Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan:
Defection-Free Collaboration between Competitors in a Learning System. CoRR abs/2406.15898 (2024) - [i326]Vincent Blot, Anastasios N. Angelopoulos, Michael I. Jordan, Nicolas J.-B. Brunel:
Automatically Adaptive Conformal Risk Control. CoRR abs/2406.17819 (2024) - [i325]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 abs/2406.19824 (2024) - [i324]Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus:
Unravelling in Collaborative Learning. CoRR abs/2407.14332 (2024) - [i323]Tianyi Lin, Chi Jin, Michael I. Jordan:
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization. CoRR abs/2408.11974 (2024) - 2023
- [j125]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) - [j124]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) - [j123]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) - [j122]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) - [j121]Bin Shi, Weijie Su, Michael I. Jordan:
On Learning Rates and Schrödinger Operators. J. Mach. Learn. Res. 24: 379:1-379:53 (2023) - [j120]Eric Xia, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan:
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning. J. Mach. Learn. Res. 24: 392:1-392:43 (2023) - [j119]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) - [j118]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): 526-532 (2023) - [j117]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) - [c397]Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab:
Competition, Alignment, and Equilibria in Digital Marketplaces. AAAI 2023: 5689-5696 - [c396]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 - [c395]Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan:
A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. AISTATS 2023: 2207-2261 - [c394]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 - [c393]Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT 2023: 1166-1215 - [c392]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. COLT 2023: 4570-4597 - [c391]Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan:
Recommendation Systems with Distribution-Free Reliability Guarantees. COPA 2023: 175-193 - [c390]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 - [c389]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 - [c388]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean:
Modeling content creator incentives on algorithm-curated platforms. ICLR 2023 - [c387]Tong Yang, Michael I. Jordan, Tatjana Chavdarova:
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method. ICLR 2023 - [c386]Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan:
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. ICML 2023: 20351-20383 - [c385]Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar:
Federated Conformal Predictors for Distributed Uncertainty Quantification. ICML 2023: 22942-22964 - [c384]Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Online Learning in Stackelberg Games with an Omniscient Follower. ICML 2023: 42304-42316 - [c383]Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. ICML 2023: 43037-43067 - [c382]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 - [c381]Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song:
On Learning Necessary and Sufficient Causal Graphs. NeurIPS 2023 - [c380]Tiffany Ding, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani:
Class-Conditional Conformal Prediction with Many Classes. NeurIPS 2023 - [c379]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning. NeurIPS 2023 - [c378]Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab:
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. NeurIPS 2023 - [c377]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 - [c376]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 - [c375]Banghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao:
Doubly-Robust Self-Training. NeurIPS 2023 - [c374]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 - [c373]Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan:
The Sample Complexity of Online Contract Design. EC 2023: 1188 - [c372]Chris Junchi Li, Michael I. Jordan:
Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. UAI 2023: 1230-1240 - [i322]Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic:
Prediction-Powered Inference. CoRR abs/2301.09633 (2023) - [i321]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. CoRR abs/2301.11270 (2023) - [i320]Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Online Learning in Stackelberg Games with an Omniscient Follower. CoRR abs/2301.11518 (2023) - [i319]Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song:
On Learning Necessary and Sufficient Causal Graphs. CoRR abs/2301.12389 (2023) - [i318]Michael Muehlebach, Michael I. Jordan:
Accelerated First-Order Optimization under Nonlinear Constraints. CoRR abs/2302.00316 (2023) - [i317]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. CoRR abs/2302.08300 (2023) - [i316]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) - [i315]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) - [i314]Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan:
Online Learning in a Creator Economy. CoRR abs/2305.11381 (2023) - [i313]Serena Lutong Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan:
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry. CoRR abs/2305.14595 (2023) - [i312]Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar:
Federated Conformal Predictors for Distributed Uncertainty Quantification. CoRR abs/2305.17564 (2023) - [i311]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) - [i310]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) - [i309]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) - [i308]Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan:
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. CoRR abs/2306.05592 (2023) - [i307]Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt:
Incentivizing High-Quality Content in Online Recommender Systems. CoRR abs/2306.07479 (2023) - [i306]Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi:
Provably Personalized and Robust Federated Learning. CoRR abs/2306.08393 (2023) - [i305]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) - [i304]Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab:
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. CoRR abs/2306.14670 (2023) - [i303]Yang Cai, Michael I. Jordan, Tianyi Lin, Argyris Oikonomou, Emmanouil V. Vlatakis-Gkaragkounis:
Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds. CoRR abs/2306.16617 (2023) - [i302]Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan:
Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR abs/2307.00126 (2023) - [i301]Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff:
Incentive-Theoretic Bayesian Inference for Collaborative Science. CoRR abs/2307.03748 (2023) - [i300]Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
Scaff-PD: Communication Efficient Fair and Robust Federated Learning. CoRR abs/2307.13381 (2023) - [i299]Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab:
Delegating Data Collection in Decentralized Machine Learning. CoRR abs/2309.01837 (2023) - [i298]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) - [i297]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) - [i296]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) - [i295]Tianyi Lin, Marco Cuturi, Michael I. Jordan:
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. CoRR abs/2310.14087 (2023) - [i294]Alireza Fallah, Michael I. Jordan:
Contract Design With Safety Inspections. CoRR abs/2311.02537 (2023) - [i293]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) - [i292]Eugene Berta, Francis R. Bach, Michael I. Jordan:
Classifier Calibration with ROC-Regularized Isotonic Regression. CoRR abs/2311.12436 (2023) - [i291]Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Michael I. Jordan:
Towards Optimal Statistical Watermarking. CoRR abs/2312.07930 (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) - [c371]Nhat Ho, Tianyi Lin, Michael I. Jordan:
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. AISTATS 2022: 896-921 - [c370]Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS 2022: 1219-1250 - [c369]Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica:
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. AISTATS 2022: 6200-6224 - [c368]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 - [c367]