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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
- 2022
[j105]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)- [c346]Nhat Ho, Tianyi Lin, Michael I. Jordan:
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. AISTATS 2022: 896-921 - [c345]Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS 2022: 1219-1250 - [c344]Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica:
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. AISTATS 2022: 6200-6224 - [c343]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 - [c342]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 - [i260]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) - [i259]Koulik Khamaru, Eric Xia, Martin J. Wainwright, Michael I. Jordan:
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning. CoRR abs/2201.08536 (2022) - [i258]Mariel A. Werner, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan:
Online Active Learning with Dynamic Marginal Gain Thresholding. CoRR abs/2201.10547 (2022) - [i257]Elynn Y. Chen, Rui Song, Michael I. Jordan:
Reinforcement Learning with Heterogeneous Data: Estimation and Inference. CoRR abs/2202.00088 (2022) - [i256]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Robust Estimation for Nonparametric Families via Generative Adversarial Networks. CoRR abs/2202.01269 (2022) - [i255]Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, Michael I. Jordan:
Conformal prediction for the design problem. CoRR abs/2202.03613 (2022) - [i254]Elynn Y. Chen, Michael I. Jordan, Sai Li:
Transferred Q-learning. CoRR abs/2202.04709 (2022) - [i253]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) - [i252]Matteo Pagliardini, Gilberto Manunza, Martin Jaggi, Michael I. Jordan, Tatjana Chavdarova:
Improving Generalization via Uncertainty Driven Perturbations. CoRR abs/2202.05737 (2022) - [i251]Wenshuo Guo, Michael I. Jordan, Ellen Vitercik:
No-Regret Learning in Partially-Informed Auctions. CoRR abs/2202.10606 (2022) - [i250]Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan:
Partial Identification with Noisy Covariates: A Robust Optimization Approach. CoRR abs/2202.10665 (2022) - [i249]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) - [i248]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) - [i247]Wenshuo Guo, Michael I. Jordan, Angela Zhou:
Off-Policy Evaluation with Policy-Dependent Optimization Response. CoRR abs/2202.12958 (2022) - [i246]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) - [i245]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) - [i244]Michael I. Jordan, Tianyi Lin, Manolis Zampetakis:
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. CoRR abs/2204.03132 (2022) - [i243]Tianyi Lin, Michael I. Jordan:
Perseus: A Simple High-Order Regularization Method for Variational Inequalities. CoRR abs/2205.03202 (2022) - [i242]Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff:
Principal-Agent Hypothesis Testing. CoRR abs/2205.06812 (2022) - [i241]Sarah 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) - [i240]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) - 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]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus:
On Component Interactions in Two-Stage Recommender Systems. NeurIPS 2021: 2744-2757 - [c326]Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt:
Learning Equilibria in Matching Markets from Bandit Feedback. NeurIPS 2021: 3323-3335 - [c325]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. NeurIPS 2021: 3401-3412 - [c324]Xiaowu Dai, Michael I. Jordan:
Learning in Multi-Stage Decentralized Matching Markets. NeurIPS 2021: 12798-12809 - [c323]Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan:
Tactical Optimism and Pessimism for Deep Reinforcement Learning. NeurIPS 2021: 12849-12863 - [c322]Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan:
Test-time Collective Prediction. NeurIPS 2021: 13719-13731 - [c321]Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan:
Who Leads and Who Follows in Strategic Classification? NeurIPS 2021: 15257-15269 - [c320]Yufeng Zhang, Siyu Chen, Zhuoran Yang, Michael I. Jordan, Zhaoran Wang:
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic. NeurIPS 2021: 15993-16006 - [c319]Wenshuo Guo, Michael I. Jordan, Emmanouil Zampetakis:
Robust Learning of Optimal Auctions. NeurIPS 2021: 21273-21284 - [c318]Ghassen Jerfel, Serena Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan:
Variational refinement for importance sampling using the forward Kullback-Leibler divergence. UAI 2021: 1819-1829 - [c317]Adelson Chua, Michael I. Jordan, Rikky Muller:
A 1.5nJ/cls Unsupervised Online Learning Classifier for Seizure Detection. VLSI Circuits 2021: 1-2 - [i239]Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, Michael I. Jordan:
Distribution-Free, Risk-Controlling Prediction Sets. CoRR abs/2101.02703 (2021) - [i238]Anastasios N. Angelopoulos, Stephen Bates, Tijana Zrnic, Michael I. Jordan:
Private Prediction Sets. CoRR abs/2102.06202 (2021) - [i237]Xiaowu Dai, Michael I. Jordan:
Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors. CoRR abs/2102.06988 (2021) - [i236]Ani Adhikari, John DeNero, Michael I. Jordan:
Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley. CoRR abs/2102.09391 (2021) - [i235]Esther Rolf, Theodora Worledge, Benjamin Recht, Michael I. Jordan:
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. CoRR abs/2103.03399 (2021) - [i234]Wenshuo Guo, Michael I. Jordan, Tianyi Lin:
A Variational Inequality Approach to Bayesian Regression Games. CoRR abs/2103.13509 (2021) - [i233]Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry:
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CoRR abs/2103.15010 (2021) - [i232]Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, Michael I. Jordan:
Multi-Source Causal Inference Using Control Variates. CoRR abs/2103.16689 (2021) - [i231]Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR abs/2104.13326 (2021) - [i230]Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan:
Parallelizing Contextual Linear Bandits. CoRR abs/2105.10590 (2021) - [i229]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. CoRR abs/2105.14363 (2021) - [i228]Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez:
PAC Best Arm Identification Under a Deadline. CoRR abs/2106.03221 (2021) - [i227]Wenshuo Guo, Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
Online Learning of Competitive Equilibria in Exchange Economies. CoRR abs/2106.06616 (2021) - [i226]Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan:
Test-time Collective Prediction. CoRR abs/2106.12012 (2021) - [i225]Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan:
Who Leads and Who Follows in Strategic Classification? CoRR abs/2106.12529 (2021) - [i224]Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg:
The Stereotyping Problem in Collaboratively Filtered Recommender Systems. CoRR abs/2106.12622 (2021) - [i223]Koulik Khamaru, Eric Xia, Martin J. Wainwright, Michael I. Jordan:
Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning. CoRR abs/2106.14352 (2021) - [i222]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus:
On component interactions in two-stage recommender systems. CoRR abs/2106.14979 (2021) - [i221]Ghassen Jerfel, Serena Wang, Clara Fannjiang, Katherine A. Heller, Yi-An Ma, Michael I. Jordan:
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence. CoRR abs/2106.15980 (2021) - [i220]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 with Restarted Iteration Averaging. CoRR abs/2107.00464 (2021) - [i219]Wenshuo Guo, Michael I. Jordan, Manolis Zampetakis:
Robust Learning of Optimal Auctions. CoRR abs/2107.06259 (2021) - [i218]Michael Muehlebach, Michael I. Jordan:
On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems. CoRR abs/2107.08225 (2021) - [i217]Mohammad Rasouli, Michael I. Jordan:
Data Sharing Markets. CoRR abs/2107.08630 (2021) - [i216]Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt:
Learning Equilibria in Matching Markets from Bandit Feedback. CoRR abs/2108.08843 (2021) - [i215]Yixin Wang, Michael I. Jordan:
Desiderata for Representation Learning: A Causal Perspective. CoRR abs/2109.03795 (2021) - [i214]Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès, Michael I. Jordan, Lihua Lei:
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control. CoRR abs/2110.01052 (2021) - [i213]Adelson Chua, Michael I. Jordan, Rikky Muller:
SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier. CoRR abs/2110.02169 (2021) - [i212]Michael I. Jordan, Keli Liu, Feng Ruan:
On the Self-Penalization Phenomenon in Feature Selection. CoRR abs/2110.05852 (2021) - [i211]Kaichao You, Yong Liu, Jianmin Wang, Michael I. Jordan, Mingsheng Long:
Ranking and Tuning Pre-trained Models: A New Paradigm of Exploiting Model Hubs. CoRR abs/2110.10545 (2021) - [i210]Reese Pathak, Rajat Sen, Nikhil Rao, N. Benjamin Erichson, Michael I. Jordan, Inderjit S. Dhillon:
Cluster-and-Conquer: A Framework For Time-Series Forecasting. CoRR abs/2110.14011 (2021) - [i209]Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. CoRR abs/2111.04873 (2021) - [i208]Tianyi Lin, Michael I. Jordan:
On Monotone Inclusions, Acceleration and Closed-Loop Control. CoRR abs/2111.08093 (2021) - [i207]Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, Michael I. Jordan:
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning. CoRR abs/2112.05923 (2021) - [i206]Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers? CoRR abs/2112.13521 (2021) - [i205]Yufeng Zhang, Siyu Chen, Zhuoran Yang, Michael I. Jordan, Zhaoran Wang:
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic. CoRR abs/2112.13530 (2021) - [i204]Tatjana Chavdarova, Michael I. Jordan, Manolis Zampetakis:
Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations. CoRR abs/2112.13826 (2021) - [i203]Xiang Li, Wenhao Yang, Zhihua Zhang, Michael I. Jordan:
Polyak-Ruppert Averaged Q-Leaning is Statistically Efficient. CoRR abs/2112.14582 (2021) - [i202]Chris Junchi Li, Michael I. Jordan:
Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems. CoRR abs/2112.14738 (2021) - 2020
- [j94]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. J. Mach. Learn. Res. 21: 43:1-43:36 (2020) - [j93]Lihua Lei, Michael I. Jordan:
On the Adaptivity of Stochastic Gradient-Based Optimization. SIAM J. Optim. 30(2): 1473-1500 (2020) - [c316]Jianbo Chen, Michael I. Jordan:
LS-Tree: Model Interpretation When the Data Are Linguistic. AAAI 2020: 3454-3461 - [c315]Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song:
Cost-Effective Incentive Allocation via Structured Counterfactual Inference. AAAI 2020: 4997-5004 - [c314]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
ML-LOO: Detecting Adversarial Examples with Feature Attribution. AAAI 2020: 6639-6647 - [c313]Lydia T. Liu, Horia Mania, Michael I. Jordan:
Competing Bandits in Matching Markets. AISTATS 2020: 1618-1628 - [c312]Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett:
Langevin Monte Carlo without smoothness. AISTATS 2020: 1716-1726 - [c311]Esther Rolf, Michael I. Jordan, Benjamin Recht:
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. AISTATS 2020: 1759-1769 - [c310]Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models. AISTATS 2020: 1866-1876 - [c309]Wenshuo Guo, Nhat Ho, Michael I. Jordan:
Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter. AISTATS 2020: 2088-2097 - [c308]Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan:
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. AISTATS 2020: 3003-3014 - [c307]Tijana Zrnic, Daniel Jiang, Aaditya Ramdas, Michael I. Jordan:
The Power of Batching in Multiple Hypothesis Testing. AISTATS 2020: 3806-3815 - [c306]Tianyi Lin, Chengyou Fan, Mengdi Wang, Michael I. Jordan:
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient. ACC 2020: 126-131 - [c305]Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry:
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games. AAMAS 2020: 860-868 - [c304]Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry:
High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. CDC 2020: 4275-4280 - [c303]Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Provably efficient reinforcement learning with linear function approximation. COLT 2020: 2137-2143 - [c302]Tianyi Lin, Chi Jin, Michael I. Jordan:
Near-Optimal Algorithms for Minimax Optimization. COLT 2020: 2738-2779 - [c301]Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. COLT 2020: 2947-2997 - [c300]Adelson Chua, Michael I. Jordan, Rikky Muller:
Unsupervised Online Learning for Long-Term High Sensitivity Seizure Detection. EMBC 2020: 528-531 - [c299]Melih Elibol, Lihua Lei, Michael I. Jordan:
Variance Reduction With Sparse Gradients. ICLR 2020 - [c298]Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan:
Stochastic Gradient and Langevin Processes. ICML 2020: 1810-1819 - [c297]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML 2020: 4880-4889 - [c296]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML 2020: 5736-5746 - [c295]Tianyi Lin, Chi Jin, Michael I. Jordan:
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. ICML 2020: 6083-6093 - [c294]