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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
[c368]Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT 2023: 1166-1215- [i299]Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic:
Prediction-Powered Inference. CoRR abs/2301.09633 (2023) - [i298]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. CoRR abs/2301.11270 (2023) - [i297]Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Online Learning in Stackelberg Games with an Omniscient Follower. CoRR abs/2301.11518 (2023) - [i296]Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song:
On Learning Necessary and Sufficient Causal Graphs. CoRR abs/2301.12389 (2023) - [i295]Michael Muehlebach, Michael I. Jordan:
Accelerated First-Order Optimization under Nonlinear Constraints. CoRR abs/2302.00316 (2023) - [i294]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. CoRR abs/2302.08300 (2023) - [i293]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) - [i292]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) - [i291]Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan:
Online Learning in a Creator Economy. CoRR abs/2305.11381 (2023) - 2022
- [j111]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) - [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) - [c367]Nhat Ho, Tianyi Lin, Michael I. Jordan:
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. AISTATS 2022: 896-921 - [c366]Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS 2022: 1219-1250 - [c365]Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica:
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. AISTATS 2022: 6200-6224 - [c364]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 - [c363]Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan:
Partial Identification with Noisy Covariates: A Robust Optimization Approach. CLeaR 2022: 318-335 - [c362]Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan:
Optimal Mean Estimation without a Variance. COLT 2022: 356-357 - [c361]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 - [c360]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 - [c359]Wenshuo Guo, Michael I. Jordan, Ellen Vitercik:
No-Regret Learning in Partially-Informed Auctions. ICML 2022: 8039-8055 - [c358]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 - [c357]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 - [c356]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Robust Estimation for Non-parametric Families via Generative Adversarial Networks. ISIT 2022: 1100-1105 - [c355]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 - [c354]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 - [c353]Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael I. Jordan, Zheng-Jun Zha:
Rank Diminishing in Deep Neural Networks. NeurIPS 2022 - [c352]Wenshuo Guo, Michael I. Jordan, Angela Zhou:
Off-Policy Evaluation with Policy-Dependent Optimization Response. NeurIPS 2022 - [c351]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
On-Demand Sampling: Learning Optimally from Multiple Distributions. NeurIPS 2022 - [c350]Michael I. Jordan, Tianyi Lin, Emmanouil-Vasileios Vlatakis-Gkaragkounis:
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. NeurIPS 2022 - [c349]Michael I. Jordan, Yixin Wang, Angela Zhou:
Empirical Gateaux Derivatives for Causal Inference. NeurIPS 2022 - [c348]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS 2022 - [c347]Tianyi Lin, Zeyu Zheng, Michael I. Jordan:
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. NeurIPS 2022 - [c346]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 - [c345]Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. NeurIPS 2022 - [c344]Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan:
Robust Calibration with Multi-domain Temperature Scaling. NeurIPS 2022 - [c343]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 - [c342]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]Tong Yang, Michael I. Jordan, Tatjana Chavdarova:
Solving Constrained Variational Inequalities via an Interior Point Method. CoRR abs/2206.10575 (2022) - [i262]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean:
Modeling Content Creator Incentives on Algorithm-Curated Platforms. CoRR abs/2206.13102 (2022) - [i261]Melih Elibol, Vinamra Benara, Samyu Yagati, Lianmin Zheng, Alvin Cheung, Michael I. Jordan, Ion Stoica:
NumS: Scalable Array Programming for the Cloud. CoRR abs/2206.14276 (2022) - [i260]Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan:
Recommendation Systems with Distribution-Free Reliability Guarantees. CoRR abs/2207.01609 (2022) - [i259]Karl Krauth, Yixin Wang, Michael I. Jordan:
Breaking Feedback Loops in Recommender Systems with Causal Inference. CoRR abs/2207.01616 (2022) - [i258]Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan:
Mechanisms that Incentivize Data Sharing in Federated Learning. CoRR abs/2207.04557 (2022) - [i257]Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. CoRR abs/2207.06343 (2022) - [i256]Tatjana Chavdarova, Ya-Ping Hsieh, Michael I. Jordan:
Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata. CoRR abs/2207.07105 (2022) - [i255]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR abs/2208.05363 (2022) - [i254]Paula Gradu, Tijana Zrnic, Yixin Wang, Michael I. Jordan:
Valid Inference after Causal Discovery. CoRR abs/2208.05949 (2022) - [i253]Michael I. Jordan, Yixin Wang, Angela Zhou:
Empirical Gateaux Derivatives for Causal Inference. CoRR abs/2208.13701 (2022) - [i252]Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab:
Competition, Alignment, and Equilibria in Digital Marketplaces. CoRR abs/2208.14423 (2022) - [i251]Tianyi Lin, Zeyu Zheng, Michael I. Jordan:
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. CoRR abs/2209.05045 (2022) - [i250]Michael I. Jordan, Tianyi Lin, Manolis Zampetakis:
On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization. CoRR abs/2209.12463 (2022) - [i249]Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan:
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. CoRR abs/2209.15634 (2022) - [i248]Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan:
QuTE: decentralized multiple testing on sensor networks with false discovery rate control. CoRR abs/2210.04334 (2022) - [i247]Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan:
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design. CoRR abs/2210.10278 (2022) - [i246]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
On-Demand Sampling: Learning Optimally from Multiple Distributions. CoRR abs/2210.12529 (2022) - [i245]Tianyi Lin, Panayotis Mertikopoulos, Michael I. Jordan:
Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee. CoRR abs/2210.12860 (2022) - [i244]Tatjana Chavdarova, Matteo Pagliardini, Tong Yang, Michael I. Jordan:
Revisiting the ACVI Method for Constrained Variational Inequalities. CoRR abs/2210.15659 (2022) - [i243]Chris Junchi Li, Angela Yuan, Gauthier Gidel, Michael I. Jordan:
Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization. CoRR abs/2210.17550 (2022) - [i242]Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan:
The Sample Complexity of Online Contract Design. CoRR abs/2211.05732 (2022) - [i241]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. CoRR abs/2211.12339 (2022) - [i240]Xiaowu Dai, Yuan Qi, Michael I. Jordan:
Incentive-Aware Recommender Systems in Two-Sided Markets. CoRR abs/2211.15381 (2022) - 2021
- [j104]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM 68(2): 11:1-11:29 (2021) - [j103]Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, Michael I. Jordan:
Distribution-free, Risk-controlling Prediction Sets. J. ACM 68(6): 43:1-43:34 (2021) - [j102]Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan:
Asynchronous Online Testing of Multiple Hypotheses. J. Mach. Learn. Res. 22: 33:1-33:39 (2021) - [j101]Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. J. Mach. Learn. Res. 22: 42:1-42:41 (2021) - [j100]Michael Muehlebach, Michael I. Jordan:
Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives. J. Mach. Learn. Res. 22: 73:1-73:50 (2021) - [j99]Ashia C. Wilson, Ben Recht, Michael I. Jordan:
A Lyapunov Analysis of Accelerated Methods in Optimization. J. Mach. Learn. Res. 22: 113:1-113:34 (2021) - [j98]Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan:
Bandit Learning in Decentralized Matching Markets. J. Mach. Learn. Res. 22: 211:1-211:34 (2021) - [j97]Xiaowu Dai, Michael I. Jordan:
Learning Strategies in Decentralized Matching Markets under Uncertain Preferences. J. Mach. Learn. Res. 22: 260:1-260:50 (2021) - [j96]Jelena Diakonikolas, Michael I. Jordan:
Generalized Momentum-Based Methods: A Hamiltonian Perspective. SIAM J. Optim. 31(1): 915-944 (2021) - [j95]Koulik Khamaru, Ashwin Pananjady, Feng Ruan, Martin J. Wainwright, Michael I. Jordan:
Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis. SIAM J. Math. Data Sci. 3(4): 1013-1040 (2021) - [c341]Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan:
Learning from eXtreme Bandit Feedback. AAAI 2021: 8732-8740 - [c340]Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan:
Robustness Guarantees for Mode Estimation with an Application to Bandits. AAAI 2021: 9277-9284 - [c339]Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan:
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. AISTATS 2021: 262-270 - [c338]Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan:
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. AISTATS 2021: 2746-2754 - [c337]Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry:
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CDC 2021: 742-749 - [c336]Wenshuo Guo, Michael I. Jordan, Tianyi Lin:
A Variational Inequality Approach to Bayesian Regression Games. CDC 2021: 795-802 - [c335]Lisa Dunlap, Kirthevasan Kandasamy, Ujval Misra, Richard Liaw, Michael I. Jordan, Ion Stoica, Joseph E. Gonzalez:
Elastic Hyperparameter Tuning on the Cloud. SoCC 2021: 33-46 - [c334]Chris Junchi Li, Michael I. Jordan:
Stochastic Approximation for Online Tensorial Independent Component Analysis. COLT 2021: 3051-3106 - [c333]Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg:
The Stereotyping Problem in Collaboratively Filtered Recommender Systems. EAAMO 2021: 6:1-6:10 - [c332]Anastasios Nikolas Angelopoulos, Stephen Bates, Michael I. Jordan, Jitendra Malik:
Uncertainty Sets for Image Classifiers using Conformal Prediction. ICLR 2021 - [c331]Esther Rolf, Theodora T. Worledge, Benjamin Recht, Michael I. Jordan:
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. ICML 2021: 9040-9051 - [c330]Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph Gonzalez:
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism. ICML 2021: 10236-10246 - [c329]Nilesh Tripuraneni, Chi Jin, Michael I. Jordan:
Provable Meta-Learning of Linear Representations. ICML 2021: 10434-10443 - [c328]Jian Zhang, Jian Tang, Yiran Chen, Jie Liu, Jieping Ye, Marilyn Wolf, Vijaykrishnan Narayanan, Mani Srivastava, Michael I. Jordan, Victor Bahl:
The 4th Artificial Intelligence of Things (AIoT) Workshop. KDD 2021: 4179-4180 - [c327]