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Michael 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]