Michael I. Jordan

964 papers A* 199A 61B 11C 2Misc 1Journal 530Unranked 148

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design. J. Mach. Learn. Res. Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan
2026	J	`jnl`	A Variational Estimator for L CoRR Eugene Berta, Sacha Braun, David Holzmüller, Francis R. Bach, Michael I. Jordan
2026	J	`jnl`	Accelerated first-order optimization under nonlinear constraints. Math. Program. Michael Muehlebach, Michael I. Jordan
2026	J	`jnl`	Betting on Equilibrium: Monitoring Strategic Behavior in Multi-Agent Systems. CoRR Etienne Gauthier, Francis R. Bach, Michael I. Jordan
2026	J	`jnl`	Breaking Feedback Loops in Recommender Systems with Causal Inference. Trans. Recomm. Syst. Karl Krauth, Yixin Wang, Michael I. Jordan
2026	J	`jnl`	Calibeating Made Simple. CoRR Yurong Chen, Zhiyi Huang, Michael I. Jordan, Haipeng Luo
2026	J	`jnl`	Dynamic Programming for Epistemic Uncertainty in Markov Decision Processes. CoRR Axel Benyamine, Julien Grand-Clément, Marek Petrik, Michael I. Jordan, Alain Durmus
2026	J	`jnl`	How Sampling Shapes LLM Alignment: From One-Shot Optima to Iterative Dynamics. CoRR Yurong Chen, Yu He, Michael I. Jordan, Fan Yao
2026	J	`jnl`	Locally Adaptive Multi-Objective Learning. CoRR Jivat Neet Kaur, Isaac Gibbs, Michael I. Jordan
2026	J	`jnl`	Multi-Armed Sequential Hypothesis Testing by Betting. CoRR Ricardo J. Sandoval, Ian Waudby-Smith, Michael I. Jordan
2026	J	`jnl`	Signaling in Data Markets via Free Samples. CoRR Nivasini Ananthakrishnan, Alireza Fallah, Michael I. Jordan
2026	J	`jnl`	Test-then-Punish: A Statistical Approach to Repeated Games. CoRR Aymeric Capitaine, Antoine Scheid, Etienne Boursier, Alain Durmus, Michael I. Jordan
2026	J	`jnl`	Towards Anytime-Valid Statistical Watermarking. CoRR Baihe Huang, Eric Xu, Kannan Ramchandran, Jiantao Jiao, Michael I. Jordan
2025	J	`jnl`	A Collectivist, Economic Perspective on AI. CoRR Michael I. Jordan
2025	J	`jnl`	A Compositional Kernel Model for Feature Learning. CoRR Feng Ruan, Keli Liu, Michael I. Jordan
2025	J	`jnl`	A General Framework for Estimating Preferences Using Response Time Data. CoRR Federico Echenique, Alireza Fallah, Michael I. Jordan
2025	J	`jnl`	A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games. Quantum Francisca Vasconcelos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos, Georgios Piliouras, Michael I. Jordan
2025	J	`jnl`	Adaptive Coverage Policies in Conformal Prediction. CoRR Etienne Gauthier, Francis R. Bach, Michael I. Jordan
2025	J	`jnl`	Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback. Oper. Res. Michael I. Jordan, Tianyi Lin, Zhengyuan Zhou
2025	J	`jnl`	An Overview of Large Language Models for Statisticians. CoRR Wenlong Ji, Weizhe Yuan, Emily J. Getzen, Kyunghyun Cho, Michael I. Jordan, Song Mei, Jason Aaron Edward Weston, Weijie J. Su, Jing Xu, Linjun Zhang
2025	A*	`conf`	AutoEval Done Right: Using Synthetic Data for Model Evaluation. ICML Pierre Boyeau, Anastasios Nikolas Angelopoulos, Tianle Li, Nir Yosef, Jitendra Malik, Michael I. Jordan
2025	A	`conf`	Automatically Adaptive Conformal Risk Control. AISTATS Vincent Blot, Anastasios Nikolas Angelopoulos, Michael I. Jordan, Nicolas J.-B. Brunel
2025	J	`jnl`	Backward Conformal Prediction. CoRR Etienne Gauthier, Francis R. Bach, Michael I. Jordan
2025	J	`jnl`	Conditional Coverage Diagnostics for Conformal Prediction. CoRR Sacha Braun, David Holzmüller, Michael I. Jordan, Francis R. Bach
2025	J	`jnl`	Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores. CoRR Jivat Neet Kaur, Michael I. Jordan, Ahmed Alaa
2025	J	`jnl`	Conformal Prediction under Lévy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations. CoRR Liviu Aolaritei, Michael I. Jordan, Youssef M. Marzouk, Zheyu Oliver Wang, Julie Zhu
2025	J	`jnl`	E-Values Expand the Scope of Conformal Prediction. CoRR Etienne Gauthier, Francis R. Bach, Michael I. Jordan
2025	A	`conf`	Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy. AISTATS Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas A. Courtade, Alireza Fallah, Michael I. Jordan
2025	J	`jnl`	Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers. CoRR Yixiao Huang, Hanlin Zhu, Tianyu Guo, Jiantao Jiao, Somayeh Sojoudi, Michael I. Jordan, Stuart Russell, Song Mei
2025	J	`jnl`	Gradient Equilibrium in Online Learning: Theory and Applications. CoRR Anastasios N. Angelopoulos, Michael I. Jordan, Ryan J. Tibshirani
2025	J	`jnl`	Gradient Equilibrium in Online Learning: Theory and Applications. J. Mach. Learn. Res. Anastasios N. Angelopoulos, Michael I. Jordan, Ryan J. Tibshirani
2025	J	`jnl`	How Do LLMs Perform Two-Hop Reasoning in Context? CoRR Tianyu Guo, Hanlin Zhu, Ruiqi Zhang, Jiantao Jiao, Song Mei, Michael I. Jordan, Stuart Russell
2025	J	`jnl`	IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis. CoRR Hanyu Li, Haoyu Liu, Tingyu Zhu, Tianyu Guo, Zeyu Zheng, Xiaotie Deng, Michael I. Jordan
2025	J	`jnl`	Instability, Computational Efficiency and Statistical Accuracy. J. Mach. Learn. Res. Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu
2025	J	`jnl`	Learning Contracts in Hierarchical Multi-Agent Systems. CoRR Antoine Scheid, Etienne Boursier, Alain Durmus, Eric Moulines, Michael I. Jordan
2025	J	`jnl`	Minimum Volume Conformal Sets for Multivariate Regression. CoRR Sacha Braun, Liviu Aolaritei, Michael I. Jordan, Francis R. Bach
2025	J	`jnl`	Multivariate Conformal Prediction via Conformalized Gaussian Scoring. CoRR Sacha Braun, Eugene Berta, Michael I. Jordan, Francis R. Bach
2025	J	`jnl`	Online Decision-Focused Learning. CoRR Aymeric Capitaine, Maxime Haddouche, Eric Moulines, Michael I. Jordan, Etienne Boursier, Alain Durmus
2025	J	`jnl`	Perseus: a simple and optimal high-order method for variational inequalities. Math. Program. Tianyi Lin, Michael I. Jordan
2025	A*	`conf`	Prediction-Aware Learning in Multi-Agent Systems. ICML Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Oliviero Durmus
2025	J	`jnl`	Prediction-Aware Learning in Multi-Agent Systems. CoRR Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus
2025	A*	`conf`	Relying on the Metrics of Evaluated Agents. WWW Serena Wang, Michael I. Jordan, Katrina Ligett, R. Preston McAfee
2025	J	`jnl`	Rethinking Early Stopping: Refine, Then Calibrate. CoRR Eugene Berta, David Holzmüller, Michael I. Jordan, Francis R. Bach
2025	J	`jnl`	Revisiting mean estimation over ℓ CoRR Liviu Aolaritei, Michael I. Jordan, Reese Pathak, Annie Ulichney
2025	J	`jnl`	Sample Complexity and Representation Ability of Test-time Scaling Paradigms. CoRR Baihe Huang, Shanda Li, Tianhao Wu, Yiming Yang, Ameet Talwalkar, Kannan Ramchandran, Michael I. Jordan, Jiantao Jiao
2025	J	`jnl`	Scalable Utility-Aware Multiclass Calibration. CoRR Mahmoud Hegazy, Michael I. Jordan, Aymeric Dieuleveut
2025	A*	`conf`	Statistical Collusion by Collectives on Learning Platforms. ICML Etienne Gauthier, Francis Bach, Michael I. Jordan
2025	J	`jnl`	Statistical Collusion by Collectives on Learning Platforms. CoRR Etienne Gauthier, Francis R. Bach, Michael I. Jordan
2025	J	`jnl`	Stochastic Optimization with Optimal Importance Sampling. CoRR Liviu Aolaritei, Bart P. G. Van Parys, Henry Lam, Michael I. Jordan
2025	J	`jnl`	Stopping Rules for Stochastic Gradient Descent via Anytime-Valid Confidence Sequences. CoRR Liviu Aolaritei, Michael I. Jordan
2025	J	`jnl`	Structured Matrix Scaling for Multi-Class Calibration. CoRR Eugene Berta, David Holzmüller, Michael I. Jordan, Francis Bach
2025	J	`jnl`	The Role of the Marketplace Operator in Inducing Competition. CoRR Tiffany Ding, Dominique Perrault-Joncas, Orit Ronen, Michael I. Jordan, Dirk Bergemann, Dean P. Foster, Omer Gottesman
2025	J	`jnl`	The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective. CoRR Michael Muehlebach, Zhiyu He, Michael I. Jordan
2025	J	`jnl`	The Statistical Fairness-Accuracy Frontier. CoRR Alireza Fallah, Michael I. Jordan, Annie Ulichney
2025	J	`jnl`	Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization. J. Mach. Learn. Res. Tianyi Lin, Chi Jin, Michael I. Jordan
2025	J	`jnl`	Understanding In-context Learning of Addition via Activation Subspaces. CoRR Xinyan Hu, Kayo Yin, Michael I. Jordan, Jacob Steinhardt, Lijie Chen
2025	J	`jnl`	Valid Selection among Conformal Sets. CoRR Mahmoud Hegazy, Liviu Aolaritei, Michael I. Jordan, Aymeric Dieuleveut
2024	J	`jnl`	A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. SIAM J. Math. Data Sci. Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2024	A*	`conf`	A Primal-Dual Approach to Solving Variational Inequalities with General Constraints. ICLR Tatjana Chavdarova, Tong Yang, Matteo Pagliardini, Michael I. Jordan
2024	A	`conf`	A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. AISTATS Tianyi Lin, Marco Cuturi, Michael I. Jordan
2024	J	`jnl`	Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs. CoRR Tianyu Guo, Druv Pai, Yu Bai, Jiantao Jiao, Michael I. Jordan, Song Mei
2024	J	`jnl`	An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints. CoRR Jordan Lekeufack, Michael I. Jordan
2024	J	`jnl`	AutoEval Done Right: Using Synthetic Data for Model Evaluation. CoRR Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik, Michael I. Jordan
2024	J	`jnl`	Automatically Adaptive Conformal Risk Control. CoRR Vincent Blot, Anastasios N. Angelopoulos, Michael I. Jordan, Nicolas J.-B. Brunel
2024	A*	`conf`	Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. ICML 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
2024	J	`jnl`	Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. CoRR 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
2024	A	`conf`	Classifier Calibration with ROC-Regularized Isotonic Regression. AISTATS Eugene Berta, Francis R. Bach, Michael I. Jordan
2024	A*	`conf`	Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. ICML Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan
2024	J	`jnl`	Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. CoRR Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan
2024	A*	`conf`	Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions. ICRA Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik
2024	A*	`conf`	Contract Design With Safety Inspections. EC Alireza Fallah, Michael I. Jordan
2024	A*	`conf`	Data Acquisition via Experimental Design for Data Markets. NeurIPS Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
2024	J	`jnl`	Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
2024	J	`jnl`	Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction. CoRR Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan
2024	J	`jnl`	Defection-Free Collaboration between Competitors in a Learning System. CoRR Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
2024	A	`conf`	Delegating Data Collection in Decentralized Machine Learning. AISTATS Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab
2024	J	`jnl`	Desiderata for Representation Learning: A Causal Perspective. J. Mach. Learn. Res. Yixin Wang, Michael I. Jordan
2024	A*	`conf`	Dimension-free Private Mean Estimation for Anisotropic Distributions. NeurIPS Yuval Dagan, Michael I. Jordan, Xuelin Yang, Lydia Zakynthinou, Nikita Zhivotovskiy
2024	J	`jnl`	Dimension-free Private Mean Estimation for Anisotropic Distributions. CoRR Yuval Dagan, Michael I. Jordan, Xuelin Yang, Lydia Zakynthinou, Nikita Zhivotovskiy
2024	J	`jnl`	Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy. CoRR Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas A. Courtade, Alireza Fallah, Michael I. Jordan
2024	A*	`conf`	Fair Allocation in Dynamic Mechanism Design. NeurIPS Alireza Fallah, Michael I. Jordan, Annie Ulichney
2024	J	`jnl`	Fair Allocation in Dynamic Mechanism Design. CoRR Alireza Fallah, Michael I. Jordan, Annie Ulichney
2024	A*	`conf`	Fairness-Aware Meta-Learning via Nash Bargaining. NeurIPS Yi Zeng, Xuelin Yang, Li Chen, Cristian Canton Ferrer, Ming Jin, Michael I. Jordan, Ruoxi Jia
2024	J	`jnl`	Fairness-Aware Meta-Learning via Nash Bargaining. CoRR Yi Zeng, Xuelin Yang, Li Chen, Cristian Canton Ferrer, Ming Jin, Michael I. Jordan, Ruoxi Jia
2024	J	`jnl`	Incentive-Aware Recommender Systems in Two-Sided Markets. Trans. Recomm. Syst. Xiaowu Dai, Wenlu Xu, Yuan Qi, Michael I. Jordan
2024	A*	`conf`	Incentivized Learning in Principal-Agent Bandit Games. ICML Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus
2024	J	`jnl`	Incentivized Learning in Principal-Agent Bandit Games. CoRR Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus
2024	J	`jnl`	Information Elicitation in Agency Games. CoRR Serena Wang, Michael I. Jordan, Katrina Ligett, R. Preston McAfee
2024	A*	`conf`	Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. ICML Banghua Zhu, Michael I. Jordan, Jiantao Jiao
2024	J	`jnl`	Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. CoRR Banghua Zhu, Michael I. Jordan, Jiantao Jiao
2024	J	`jnl`	Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach. J. Mach. Learn. Res. Shuang Qiu, Boxiang Lyu, Qinglin Meng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan
2024	J	`jnl`	Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity. CoRR Eric Zhao, Tatjana Chavdarova, Michael I. Jordan
2024	A*	`conf`	Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality. NeurIPS Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus
2024	J	`jnl`	Mitigating Externalities while Learning: an Online Version of the Coase Theorem. CoRR Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus
2024	A	`conf`	On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry. AISTATS Serena Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan
2024	J	`jnl`	On Three-Layer Data Markets. CoRR Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian
2024	J	`jnl`	Optimal Design for Reward Modeling in RLHF. CoRR Antoine Scheid, Etienne Boursier, Alain Durmus, Michael I. Jordan, Pierre Ménard, Eric Moulines, Michal Valko
2024	—	`conf`	Privacy Can Arise Endogenously in an Economic System with Learning Agents. FORC Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
2024	J	`jnl`	Privacy Can Arise Endogenously in an Economic System with Learning Agents. CoRR Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
2024	J	`jnl`	Reduced-Rank Multi-objective Policy Learning and Optimization. CoRR Ezinne Nwankwo, Michael I. Jordan, Angela Zhou
2024	J	`jnl`	Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry. CoRR Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt
2024	J	`jnl`	The Limits of Price Discrimination Under Privacy Constraints. CoRR Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian
2024	A*	`conf`	Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. NeurIPS Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart J. Russell
2024	J	`jnl`	Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. CoRR Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart Russell
2024	J	`jnl`	Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization. CoRR Tianyi Lin, Chi Jin, Michael I. Jordan
2024	A*	`conf`	Unravelling in Collaborative Learning. NeurIPS Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus
2024	J	`jnl`	Unravelling in Collaborative Learning. CoRR Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus
2023	A*	`conf`	A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. ICLR Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan
2023	J	`jnl`	A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning. CoRR Neha S. Wadia, Yatin Dandi, Michael I. Jordan
2023	J	`jnl`	A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games. CoRR Francisca Vasconcelos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos, Georgios Piliouras, Michael I. Jordan
2023	J	`jnl`	A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. CoRR Tianyi Lin, Marco Cuturi, Michael I. Jordan
2023	A	`conf`	A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. AISTATS Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan
2023	A*	`conf`	A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning. NeurIPS Nika Haghtalab, Michael I. Jordan, Eric Zhao
2023	J	`jnl`	A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning. CoRR Nika Haghtalab, Michael I. Jordan, Eric Zhao
2023	J	`jnl`	Accelerated First-Order Optimization under Nonlinear Constraints. CoRR Michael Muehlebach, Michael I. Jordan
2023	J	`jnl`	Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan
2023	J	`jnl`	Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback. CoRR Michael I. Jordan, Tianyi Lin, Zhengyuan Zhou
2023	B	`conf`	An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2023	A	`conf`	Byzantine-Robust Federated Learning with Optimal Statistical Rates. AISTATS Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan
2023	J	`jnl`	Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? J. Mach. Learn. Res. Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
2023	A*	`conf`	Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback. OSDI Romil Bhardwaj, Kirthevasan Kandasamy, Asim Biswal, Wenshuo Guo, Benjamin Hindman, Joseph Gonzalez, Michael I. Jordan, Ion Stoica
2023	J	`jnl`	Class-Conditional Conformal Prediction With Many Classes. CoRR Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani
2023	A*	`conf`	Class-Conditional Conformal Prediction with Many Classes. NeurIPS Tiffany Ding, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani
2023	J	`jnl`	Classifier Calibration with ROC-Regularized Isotonic Regression. CoRR Eugene Berta, Francis R. Bach, Michael I. Jordan
2023	A*	`conf`	Competition, Alignment, and Equilibria in Digital Marketplaces. AAAI Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab
2023	J	`jnl`	Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions. CoRR Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik
2023	J	`jnl`	Contract Design With Safety Inspections. CoRR Alireza Fallah, Michael I. Jordan
2023	J	`jnl`	Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds. CoRR Yang Cai, Michael I. Jordan, Tianyi Lin, Argyris Oikonomou, Emmanouil V. Vlatakis-Gkaragkounis
2023	J	`jnl`	Delegating Data Collection in Decentralized Machine Learning. CoRR Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab
2023	A*	`conf`	Deterministic Nonsmooth Nonconvex Optimization. COLT Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis
2023	J	`jnl`	Deterministic Nonsmooth Nonconvex Optimization. CoRR Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis
2023	J	`jnl`	Doubly Robust Self-Training. CoRR Banghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao
2023	A*	`conf`	Doubly-Robust Self-Training. NeurIPS Banghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao
2023	J	`jnl`	Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. CoRR Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan
2023	A*	`conf`	Federated Conformal Predictors for Distributed Uncertainty Quantification. ICML Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
2023	J	`jnl`	Federated Conformal Predictors for Distributed Uncertainty Quantification. CoRR Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
2023	A	`conf`	Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning. AISTATS Ruitu Xu, Yifei Min, Tianhao Wang, Michael I. Jordan, Zhaoran Wang, Zhuoran Yang
2023	J	`jnl`	Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning. CoRR Ruitu Xu, Yifei Min, Tianhao Wang, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang
2023	J	`jnl`	Fine-Tuning Language Models with Advantage-Induced Policy Alignment. CoRR Banghua Zhu, Hiteshi Sharma, Felipe Vieira Frujeri, Shi Dong, Chenguang Zhu, Michael I. Jordan, Jiantao Jiao
2023	J	`jnl`	First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. J. Mach. Learn. Res. Michael I. Jordan, Tianyi Lin, Manolis Zampetakis
2023	A*	`conf`	Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. NeurIPS Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab
2023	J	`jnl`	Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. CoRR Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab
2023	J	`jnl`	Incentive-Theoretic Bayesian Inference for Collaborative Science. CoRR Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff
2023	J	`jnl`	Incentivizing High-Quality Content in Online Recommender Systems. CoRR Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt
2023	J	`jnl`	Instance-Dependent Confidence and Early Stopping for Reinforcement Learning. J. Mach. Learn. Res. Eric Xia, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan
2023	A*	`conf`	KDD-2023 Workshop on Decision Intelligence and Analytics for Online Marketplaces. KDD Zhiwei (Tony) Qin, Rui Song, Jieping Ye, Hongtu Zhu, Michael I. Jordan
2023	J	`jnl`	Learning Equilibria in Matching Markets with Bandit Feedback. J. ACM Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt
2023	A*	`conf`	Modeling content creator incentives on algorithm-curated platforms. ICLR Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean
2023	J	`jnl`	Monotone Inclusions, Acceleration, and Closed-Loop Control. Math. Oper. Res. Tianyi Lin, Michael I. Jordan
2023	A*	`conf`	Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. ICML Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan
2023	A*	`conf`	Neural Dependencies Emerging from Learning Massive Categories. CVPR Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael I. Jordan, Zheng-Jun Zha
2023	A	`conf`	Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. UAI Chris Junchi Li, Michael I. Jordan
2023	A*	`conf`	On Learning Necessary and Sufficient Causal Graphs. NeurIPS Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song
2023	J	`jnl`	On Learning Necessary and Sufficient Causal Graphs. CoRR Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song
2023	J	`jnl`	On Learning Rates and Schrödinger Operators. J. Mach. Learn. Res. Bin Shi, Weijie Su, Michael I. Jordan
2023	J	`jnl`	On Optimal Caching and Model Multiplexing for Large Model Inference. CoRR Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark W. Barrett, Michael I. Jordan, Jiantao Jiao
2023	A*	`conf`	Online Learning in Stackelberg Games with an Omniscient Follower. ICML Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan
2023	J	`jnl`	Online Learning in Stackelberg Games with an Omniscient Follower. CoRR Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan
2023	J	`jnl`	Online Learning in a Creator Economy. CoRR Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan
2023	J	`jnl`	Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry. CoRR Serena Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan
2023	A*	`conf`	Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure. NeurIPS Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S. Du
2023	J	`jnl`	Prediction-Powered Inference. CoRR Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic
2023	A*	`conf`	Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. ICML Banghua Zhu, Michael I. Jordan, Jiantao Jiao
2023	J	`jnl`	Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. CoRR Banghua Zhu, Jiantao Jiao, Michael I. Jordan
2023	J	`jnl`	Provably Efficient Reinforcement Learning with Linear Function Approximation. Math. Oper. Res. Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
2023	J	`jnl`	Provably Personalized and Robust Federated Learning. CoRR Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi
2023	J	`jnl`	Provably Personalized and Robust Federated Learning. Trans. Mach. Learn. Res. Mariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy
2023	—	`conf`	Recommendation Systems with Distribution-Free Reliability Guarantees. COPA Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan
2023	J	`jnl`	Scaff-PD: Communication Efficient Fair and Robust Federated Learning. CoRR Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
2023	J	`jnl`	Skilful nowcasting of extreme precipitation with NowcastNet. Nat. Yuchen Zhang, Mingsheng Long, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael I. Jordan, Jianmin Wang
2023	A*	`conf`	Solving Constrained Variational Inequalities via a First-order Interior Point-based Method. ICLR Tong Yang, Michael I. Jordan, Tatjana Chavdarova
2023	A*	`conf`	The Sample Complexity of Online Contract Design. EC Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan
2023	A*	`conf`	Towards Optimal Caching and Model Selection for Large Model Inference. NeurIPS Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark W. Barrett, Michael I. Jordan, Jiantao Jiao
2023	J	`jnl`	Towards Optimal Statistical Watermarking. CoRR Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Michael I. Jordan
2023	J	`jnl`	VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback. J. Mach. Learn. Res. Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica
2022	J	`jnl`	A Continuous-Time Perspective on Monotone Equation Problems. CoRR Tianyi Lin, Michael I. Jordan
2022	J	`jnl`	A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. CoRR Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan
2022	J	`jnl`	A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design. CoRR Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan
2022	J	`jnl`	A control-theoretic perspective on optimal high-order optimization. Math. Program. Tianyi Lin, Michael I. Jordan
2022	J	`jnl`	Active Learning for Nonlinear System Identification with Guarantees. J. Mach. Learn. Res. Horia Mania, Michael I. Jordan, Benjamin Recht
2022	J	`jnl`	Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. SIAM J. Math. Data Sci. Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan
2022	J	`jnl`	Breaking Feedback Loops in Recommender Systems with Causal Inference. CoRR Karl Krauth, Yixin Wang, Michael I. Jordan
2022	J	`jnl`	Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees. CoRR Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan
2022	J	`jnl`	Competition, Alignment, and Equilibria in Digital Marketplaces. CoRR Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab
2022	J	`jnl`	Conformal prediction for the design problem. CoRR Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, Michael I. Jordan
2022	J	`jnl`	Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata. CoRR Tatjana Chavdarova, Ya-Ping Hsieh, Michael I. Jordan
2022	J	`jnl`	Convergence Rates for Gaussian Mixtures of Experts. J. Mach. Learn. Res. Nhat Ho, Chiao-Yu Yang, Michael I. Jordan
2022	A*	`conf`	Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail and Beyond. KDD Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan
2022	A*	`conf`	Empirical Gateaux Derivatives for Causal Inference. NeurIPS Michael I. Jordan, Yixin Wang, Angela Zhou
2022	J	`jnl`	Empirical Gateaux Derivatives for Causal Inference. CoRR Michael I. Jordan, Yixin Wang, Angela Zhou
2022	J	`jnl`	Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee. CoRR Tianyi Lin, Panayotis Mertikopoulos, Michael I. Jordan
2022	A	`conf`	Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan
2022	A*	`conf`	First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. NeurIPS Michael I. Jordan, Tianyi Lin, Emmanouil V. Vlatakis-Gkaragkounis
2022	J	`jnl`	First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. CoRR Michael I. Jordan, Tianyi Lin, Emmanouil V. Vlatakis-Gkaragkounis
2022	J	`jnl`	First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. CoRR Michael I. Jordan, Tianyi Lin, Manolis Zampetakis
2022	J	`jnl`	Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents. CoRR Alessandro Barp, Lancelot Da Costa, Guilherme França, Karl J. Friston, Mark A. Girolami, Michael I. Jordan, Grigorios A. Pavliotis
2022	A*	`conf`	Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. NeurIPS Tianyi Lin, Zeyu Zheng, Michael I. Jordan
2022	J	`jnl`	Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. CoRR Tianyi Lin, Zeyu Zheng, Michael I. Jordan
2022	B	`conf`	Identifying Systematic Variation at the Single-Cell Level by Leveraging Low-Resolution Population-Level Data. RECOMB Elior Rahmani, Michael I. Jordan, Nir Yosef
2022	A*	`conf`	Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. ICML Anastasios N. Angelopoulos, Amit Pal Singh Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano
2022	J	`jnl`	Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. CoRR Anastasios N. Angelopoulos, Amit P. S. Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano
2022	J	`jnl`	Improving Generalization via Uncertainty Driven Perturbations. CoRR Matteo Pagliardini, Gilberto Manunza, Martin Jaggi, Michael I. Jordan, Tatjana Chavdarova
2022	J	`jnl`	Incentive-Aware Recommender Systems in Two-Sided Markets. CoRR Xiaowu Dai, Yuan Qi, Michael I. Jordan
2022	J	`jnl`	Instance-Dependent Confidence and Early Stopping for Reinforcement Learning. CoRR Koulik Khamaru, Eric Xia, Martin J. Wainwright, Michael I. Jordan
2022	J	`jnl`	KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail, and Beyond. SIGKDD Explor. Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan
2022	A*	`conf`	Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. NeurIPS Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang
2022	J	`jnl`	Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. CoRR Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang
2022	A	`conf`	Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. AISTATS Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica
2022	J	`jnl`	Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach. CoRR Boxiang Lyu, Qinglin Meng, Shuang Qiu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan
2022	A*	`conf`	Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
2022	J	`jnl`	Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
2022	J	`jnl`	Mechanisms that Incentivize Data Sharing in Federated Learning. CoRR Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan
2022	J	`jnl`	Modeling Content Creator Incentives on Algorithm-Curated Platforms. CoRR Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean
2022	J	`jnl`	Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias. Trans. Mach. Learn. Res. Wenshuo Guo, Serena Lutong Wang, Peng Ding, Yixin Wang, Michael I. Jordan
2022	J	`jnl`	Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization. CoRR Chris Junchi Li, Angela Yuan, Gauthier Gidel, Michael I. Jordan
2022	J	`jnl`	Neural Dependencies Emerging from Learning Massive Categories. CoRR Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael I. Jordan, Zheng-Jun Zha
2022	A*	`conf`	No-Regret Learning in Partially-Informed Auctions. ICML Wenshuo Guo, Michael I. Jordan, Ellen Vitercik
2022	J	`jnl`	No-Regret Learning in Partially-Informed Auctions. CoRR Wenshuo Guo, Michael I. Jordan, Ellen Vitercik
2022	J	`jnl`	NumS: Scalable Array Programming for the Cloud. CoRR Melih Elibol, Vinamra Benara, Samyu Yagati, Lianmin Zheng, Alvin Cheung, Michael I. Jordan, Ion Stoica
2022	A*	`conf`	Off-Policy Evaluation with Policy-Dependent Optimization Response. NeurIPS Wenshuo Guo, Michael I. Jordan, Angela Zhou
2022	J	`jnl`	Off-Policy Evaluation with Policy-Dependent Optimization Response. CoRR Wenshuo Guo, Michael I. Jordan, Angela Zhou
2022	J	`jnl`	On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems. J. Mach. Learn. Res. Michael Muehlebach, Michael I. Jordan
2022	A	`conf`	On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. AISTATS Nhat Ho, Tianyi Lin, Michael I. Jordan
2022	J	`jnl`	On the Complexity of Approximating Multimarginal Optimal Transport. J. Mach. Learn. Res. Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan
2022	J	`jnl`	On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization. CoRR Michael I. Jordan, Tianyi Lin, Manolis Zampetakis
2022	A	`conf`	On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. AISTATS Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
2022	J	`jnl`	On the Efficiency of Entropic Regularized Algorithms for Optimal Transport. J. Mach. Learn. Res. Tianyi Lin, Nhat Ho, Michael I. Jordan
2022	A*	`conf`	On-Demand Sampling: Learning Optimally from Multiple Distributions. NeurIPS Nika Haghtalab, Michael I. Jordan, Eric Zhao
2022	J	`jnl`	On-Demand Sampling: Learning Optimally from Multiple Distributions. CoRR Nika Haghtalab, Michael I. Jordan, Eric Zhao
2022	J	`jnl`	Online Active Learning with Dynamic Marginal Gain Thresholding. CoRR Mariel A. Werner, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan
2022	A*	`conf`	Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. ICML Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
2022	J	`jnl`	Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. CoRR Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
2022	J	`jnl`	Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. CoRR Simon S. Du, Gauthier Gidel, Michael I. Jordan, Chris Junchi Li
2022	A*	`conf`	Optimal Mean Estimation without a Variance. COLT Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan
2022	J	`jnl`	Optimal variance-reduced stochastic approximation in Banach spaces. CoRR Wenlong Mou, Koulik Khamaru, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2022	—	`conf`	Partial Identification with Noisy Covariates: A Robust Optimization Approach. CLeaR Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan
2022	J	`jnl`	Partial Identification with Noisy Covariates: A Robust Optimization Approach. CoRR Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan
2022	J	`jnl`	Perseus: A Simple High-Order Regularization Method for Variational Inequalities. CoRR Tianyi Lin, Michael I. Jordan
2022	J	`jnl`	Principal-Agent Hypothesis Testing. CoRR Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff
2022	J	`jnl`	QuTE: decentralized multiple testing on sensor networks with false discovery rate control. CoRR Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan
2022	A*	`conf`	ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. COLT Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan
2022	A*	`conf`	Rank Diminishing in Deep Neural Networks. NeurIPS Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael I. Jordan, Zheng-Jun Zha
2022	J	`jnl`	Rank Diminishing in Deep Neural Networks. CoRR Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael I. Jordan, Zheng-Jun Zha
2022	J	`jnl`	Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs. J. Mach. Learn. Res. Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long
2022	J	`jnl`	Recommendation Systems with Distribution-Free Reliability Guarantees. CoRR Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan
2022	J	`jnl`	Reinforcement Learning with Heterogeneous Data: Estimation and Inference. CoRR Elynn Y. Chen, Rui Song, Michael I. Jordan
2022	J	`jnl`	Revisiting the ACVI Method for Constrained Variational Inequalities. CoRR Tatjana Chavdarova, Matteo Pagliardini, Tong Yang, Michael I. Jordan
2022	A*	`conf`	Robust Calibration with Multi-domain Temperature Scaling. NeurIPS Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan
2022	J	`jnl`	Robust Calibration with Multi-domain Temperature Scaling. CoRR Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan
2022	B	`conf`	Robust Estimation for Non-parametric Families via Generative Adversarial Networks. ISIT Banghua Zhu, Jiantao Jiao, Michael I. Jordan
2022	J	`jnl`	Robust Estimation for Nonparametric Families via Generative Adversarial Networks. CoRR Banghua Zhu, Jiantao Jiao, Michael I. Jordan
2022	J	`jnl`	SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier. IEEE J. Solid State Circuits Adelson Chua, Michael I. Jordan, Rikky Muller
2022	A*	`conf`	Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. EC Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu
2022	J	`jnl`	Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. CoRR Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu
2022	J	`jnl`	Solving Constrained Variational Inequalities via an Interior Point Method. CoRR Tong Yang, Michael I. Jordan, Tatjana Chavdarova
2022	A*	`conf`	TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. NeurIPS Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
2022	J	`jnl`	TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. CoRR Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
2022	A*	`conf`	The 5th Artificial Intelligence of Things (AIoT) Workshop. KDD Jian Zhang, Jian Tang, Yiran Chen, Jie Liu, Jieping Ye, Marilyn Wolf, Vijaykrishnan Narayanan, Mani B. Srivastava, Michael I. Jordan, Victor Bahl
2022	J	`jnl`	The Sample Complexity of Online Contract Design. CoRR Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan
2022	J	`jnl`	The Sky Above The Clouds. CoRR 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
2022	J	`jnl`	Transferred Q-learning. CoRR Elynn Y. Chen, Michael I. Jordan, Sai Li
2022	J	`jnl`	Understanding the acceleration phenomenon via high-resolution differential equations. Math. Program. Bin Shi, Simon S. Du, Michael I. Jordan, Weijie J. Su
2022	J	`jnl`	Valid Inference after Causal Discovery. CoRR Paula Gradu, Tijana Zrnic, Yixin Wang, Michael I. Jordan
2022	A*	`conf`	Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy. ICML Zhihan Liu, Miao Lu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang
2021	—	`conf`	A 1.5nJ/cls Unsupervised Online Learning Classifier for Seizure Detection. VLSI Circuits Adelson Chua, Michael I. Jordan, Rikky Muller
2021	J	`jnl`	A Lyapunov Analysis of Accelerated Methods in Optimization. J. Mach. Learn. Res. Ashia C. Wilson, Ben Recht, Michael I. Jordan
2021	—	`conf`	A Variational Inequality Approach to Bayesian Regression Games. CDC Wenshuo Guo, Michael I. Jordan, Tianyi Lin
2021	J	`jnl`	A Variational Inequality Approach to Bayesian Regression Games. CoRR Wenshuo Guo, Michael I. Jordan, Tianyi Lin
2021	J	`jnl`	An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. CoRR Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2021	J	`jnl`	Asynchronous Online Testing of Multiple Hypotheses. J. Mach. Learn. Res. Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan
2021	J	`jnl`	Bandit Learning in Decentralized Matching Markets. J. Mach. Learn. Res. Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan
2021	J	`jnl`	Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers? CoRR Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
2021	J	`jnl`	Cluster-and-Conquer: A Framework For Time-Series Forecasting. CoRR Reese Pathak, Rajat Sen, Nikhil Rao, N. Benjamin Erichson, Michael I. Jordan, Inderjit S. Dhillon
2021	J	`jnl`	Data Sharing Markets. CoRR Mohammad Rasouli, Michael I. Jordan
2021	J	`jnl`	Desiderata for Representation Learning: A Causal Perspective. CoRR Yixin Wang, Michael I. Jordan
2021	J	`jnl`	Distribution-Free, Risk-Controlling Prediction Sets. CoRR Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, Michael I. Jordan
2021	J	`jnl`	Distribution-free, Risk-controlling Prediction Sets. J. ACM Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, Michael I. Jordan
2021	A	`conf`	Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. AISTATS Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan
2021	—	`conf`	Elastic Hyperparameter Tuning on the Cloud. SoCC Lisa Dunlap, Kirthevasan Kandasamy, Ujval Misra, Richard Liaw, Michael I. Jordan, Ion Stoica, Joseph E. Gonzalez
2021	J	`jnl`	ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning. CoRR Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, Michael I. Jordan
2021	J	`jnl`	Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan
2021	J	`jnl`	Generalized Momentum-Based Methods: A Hamiltonian Perspective. SIAM J. Optim. Jelena Diakonikolas, Michael I. Jordan
2021	J	`jnl`	High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. J. Mach. Learn. Res. Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2021	J	`jnl`	Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning. CoRR Koulik Khamaru, Eric Xia, Martin J. Wainwright, Michael I. Jordan
2021	J	`jnl`	Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley. CoRR Ani Adhikari, John DeNero, Michael I. Jordan
2021	J	`jnl`	Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis. SIAM J. Math. Data Sci. Koulik Khamaru, Ashwin Pananjady, Feng Ruan, Martin J. Wainwright, Michael I. Jordan
2021	J	`jnl`	Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations. CoRR Tatjana Chavdarova, Michael I. Jordan, Manolis Zampetakis
2021	J	`jnl`	Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control. CoRR Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès, Michael I. Jordan, Lihua Lei
2021	A*	`conf`	Learning Equilibria in Matching Markets from Bandit Feedback. NeurIPS Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt
2021	J	`jnl`	Learning Equilibria in Matching Markets from Bandit Feedback. CoRR Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt
2021	J	`jnl`	Learning Strategies in Decentralized Matching Markets under Uncertain Preferences. J. Mach. Learn. Res. Xiaowu Dai, Michael I. Jordan
2021	A*	`conf`	Learning from eXtreme Bandit Feedback. AAAI Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan
2021	A*	`conf`	Learning in Multi-Stage Decentralized Matching Markets. NeurIPS Xiaowu Dai, Michael I. Jordan
2021	J	`jnl`	Multi-Source Causal Inference Using Control Variates. CoRR Wenshuo Guo, Serena Lutong Wang, Peng Ding, Yixin Wang, Michael I. Jordan
2021	J	`jnl`	Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors. CoRR Xiaowu Dai, Michael I. Jordan
2021	J	`jnl`	Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems. CoRR Chris Junchi Li, Michael I. Jordan
2021	A*	`conf`	On Component Interactions in Two-Stage Recommender Systems. NeurIPS Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus
2021	J	`jnl`	On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems. CoRR Michael Muehlebach, Michael I. Jordan
2021	J	`jnl`	On Monotone Inclusions, Acceleration and Closed-Loop Control. CoRR Tianyi Lin, Michael I. Jordan
2021	J	`jnl`	On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2021	A	`conf`	On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. AISTATS Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan
2021	J	`jnl`	On component interactions in two-stage recommender systems. CoRR Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus
2021	J	`jnl`	On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. CoRR Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
2021	J	`jnl`	On the Self-Penalization Phenomenon in Feature Selection. CoRR Michael I. Jordan, Keli Liu, Feng Ruan
2021	—	`conf`	On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CDC Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry
2021	J	`jnl`	On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CoRR Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry
2021	A*	`conf`	On the Theory of Reinforcement Learning with Once-per-Episode Feedback. NeurIPS Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2021	J	`jnl`	On the Theory of Reinforcement Learning with Once-per-Episode Feedback. CoRR Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2021	J	`jnl`	Online Learning of Competitive Equilibria in Exchange Economies. CoRR Wenshuo Guo, Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica
2021	J	`jnl`	Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives. J. Mach. Learn. Res. Michael Muehlebach, Michael I. Jordan
2021	J	`jnl`	PAC Best Arm Identification Under a Deadline. CoRR Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez
2021	J	`jnl`	Parallelizing Contextual Linear Bandits. CoRR Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan
2021	J	`jnl`	Polyak-Ruppert Averaged Q-Leaning is Statistically Efficient. CoRR Xiang Li, Wenhao Yang, Zhihua Zhang, Michael I. Jordan
2021	J	`jnl`	Private Prediction Sets. CoRR Anastasios N. Angelopoulos, Stephen Bates, Tijana Zrnic, Michael I. Jordan
2021	A*	`conf`	Provable Meta-Learning of Linear Representations. ICML Nilesh Tripuraneni, Chi Jin, Michael I. Jordan
2021	J	`jnl`	Ranking and Tuning Pre-trained Models: A New Paradigm of Exploiting Model Hubs. CoRR Kaichao You, Yong Liu, Jianmin Wang, Michael I. Jordan, Mingsheng Long
2021	A*	`conf`	Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. ICML Esther Rolf, Theodora T. Worledge, Benjamin Recht, Michael I. Jordan
2021	J	`jnl`	Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. CoRR Esther Rolf, Theodora Worledge, Benjamin Recht, Michael I. Jordan
2021	A*	`conf`	Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism. ICML Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph Gonzalez
2021	A*	`conf`	Robust Learning of Optimal Auctions. NeurIPS Wenshuo Guo, Michael I. Jordan, Emmanouil Zampetakis
2021	J	`jnl`	Robust Learning of Optimal Auctions. CoRR Wenshuo Guo, Michael I. Jordan, Manolis Zampetakis
2021	A*	`conf`	Robustness Guarantees for Mode Estimation with an Application to Bandits. AAAI Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
2021	J	`jnl`	SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier. CoRR Adelson Chua, Michael I. Jordan, Rikky Muller
2021	A*	`conf`	Stochastic Approximation for Online Tensorial Independent Component Analysis. COLT Chris Junchi Li, Michael I. Jordan
2021	A*	`conf`	Tactical Optimism and Pessimism for Deep Reinforcement Learning. NeurIPS Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan
2021	A*	`conf`	Test-time Collective Prediction. NeurIPS Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan
2021	J	`jnl`	Test-time Collective Prediction. CoRR Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan
2021	A*	`conf`	The 4th Artificial Intelligence of Things (AIoT) Workshop. KDD Jian Zhang, Jian Tang, Yiran Chen, Jie Liu, Jieping Ye, Marilyn Wolf, Vijaykrishnan Narayanan, Mani Srivastava, Michael I. Jordan, Victor Bahl
2021	—	`conf`	The Stereotyping Problem in Collaboratively Filtered Recommender Systems. EAAMO Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg
2021	J	`jnl`	The Stereotyping Problem in Collaboratively Filtered Recommender Systems. CoRR Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg
2021	A*	`conf`	Uncertainty Sets for Image Classifiers using Conformal Prediction. ICLR Anastasios Nikolas Angelopoulos, Stephen Bates, Michael I. Jordan, Jitendra Malik
2021	J	`jnl`	Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence. CoRR Ghassen Jerfel, Serena Lutong Wang, Clara Fannjiang, Katherine A. Heller, Yi-An Ma, Michael I. Jordan
2021	A	`conf`	Variational refinement for importance sampling using the forward Kullback-Leibler divergence. UAI Ghassen Jerfel, Serena Lutong Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan
2021	A*	`conf`	Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic. NeurIPS Yufeng Zhang, Siyu Chen, Zhuoran Yang, Michael I. Jordan, Zhaoran Wang
2021	J	`jnl`	Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic. CoRR Yufeng Zhang, Siyu Chen, Zhuoran Yang, Michael I. Jordan, Zhaoran Wang
2021	A*	`conf`	Who Leads and Who Follows in Strategic Classification? NeurIPS Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan
2021	J	`jnl`	Who Leads and Who Follows in Strategic Classification? CoRR Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan
2020	A*	`conf`	Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	Accelerated Message Passing for Entropy-Regularized MAP Inference. CoRR Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	Active Learning for Nonlinear System Identification with Guarantees. CoRR Horia Mania, Michael I. Jordan, Benjamin Recht
2020	J	`jnl`	Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. CoRR Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan
2020	J	`jnl`	Bandit Learning in Decentralized Matching Markets. CoRR Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan
2020	J	`jnl`	Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations. CoRR Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan
2020	A	`conf`	Competing Bandits in Matching Markets. AISTATS Lydia T. Liu, Horia Mania, Michael I. Jordan
2020	A*	`conf`	Continuous-time Lower Bounds for Gradient-based Algorithms. ICML Michael Muehlebach, Michael I. Jordan
2020	J	`jnl`	Continuous-time Lower Bounds for Gradient-based Algorithms. CoRR Michael Muehlebach, Michael I. Jordan
2020	A	`conf`	Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. AISTATS Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan
2020	A*	`conf`	Cost-Effective Incentive Allocation via Structured Counterfactual Inference. AAAI Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song
2020	A*	`conf`	Decision-Making with Auto-Encoding Variational Bayes. NeurIPS Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier
2020	J	`jnl`	Decision-Making with Auto-Encoding Variational Bayes. CoRR Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier
2020	J	`jnl`	Do Offline Metrics Predict Online Performance in Recommender Systems? CoRR Karl Krauth, Sarah Dean, Alex Zhao, Wenshuo Guo, Mihaela Curmei, Benjamin Recht, Michael I. Jordan
2020	J	`jnl`	Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. CoRR Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan
2020	J	`jnl`	Exploration in two-stage recommender systems. CoRR Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus
2020	A	`conf`	Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter. AISTATS Wenshuo Guo, Nhat Ho, Michael I. Jordan
2020	J	`jnl`	Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty. CoRR Wenshuo Guo, Mihaela Curmei, Serena Lutong Wang, Benjamin Recht, Michael I. Jordan
2020	A*	`conf`	Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. ICML Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan
2020	J	`jnl`	Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. CoRR Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan
2020	A*	`conf`	Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. NeurIPS Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan
2020	J	`jnl`	Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. J. Mach. Learn. Res. Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan
2020	—	`conf`	High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. CDC Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry
2020	A*	`conf`	HopSkipJumpAttack: A Query-Efficient Decision-Based Attack. SP Jianbo Chen, Michael I. Jordan, Martin J. Wainwright
2020	C	`conf`	Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient. ACC Tianyi Lin, Chengyou Fan, Mengdi Wang, Michael I. Jordan
2020	J	`jnl`	Instability, Computational Efficiency and Statistical Accuracy. CoRR Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu
2020	J	`jnl`	Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis. CoRR Koulik Khamaru, Ashwin Pananjady, Feng Ruan, Martin J. Wainwright, Michael I. Jordan
2020	A*	`conf`	LS-Tree: Model Interpretation When the Data Are Linguistic. AAAI Jianbo Chen, Michael I. Jordan
2020	A	`conf`	Langevin Monte Carlo without smoothness. AISTATS Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett
2020	J	`jnl`	Learning Strategies in Decentralized Matching Markets under Uncertain Preferences. CoRR Xiaowu Dai, Michael I. Jordan
2020	J	`jnl`	Learning from eXtreme Bandit Feedback. CoRR Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan
2020	A*	`conf`	Learning to Score Behaviors for Guided Policy Optimization. ICML Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan
2020	J	`jnl`	Lower bounds in multiple testing: A framework based on derandomized proxies. CoRR Max Rabinovich, Michael I. Jordan, Martin J. Wainwright
2020	A*	`conf`	ML-LOO: Detecting Adversarial Examples with Feature Attribution. AAAI Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan
2020	J	`jnl`	Manifold Learning via Manifold Deflation. CoRR Daniel Ting, Michael I. Jordan
2020	J	`jnl`	Mechanism Design with Bandit Feedback. CoRR Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica
2020	A*	`conf`	Near-Optimal Algorithms for Minimax Optimization. COLT Tianyi Lin, Chi Jin, Michael I. Jordan
2020	J	`jnl`	Near-Optimal Algorithms for Minimax Optimization. CoRR Tianyi Lin, Chi Jin, Michael I. Jordan
2020	A*	`conf`	On Approximate Thompson Sampling with Langevin Algorithms. ICML Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett
2020	A*	`conf`	On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. ICML Tianyi Lin, Chi Jin, Michael I. Jordan
2020	J	`jnl`	On Learning Rates and Schrödinger Operators. CoRR Bin Shi, Weijie J. Su, Michael I. Jordan
2020	A*	`conf`	On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. COLT Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. CoRR Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	On Localized Discrepancy for Domain Adaptation. CoRR Yuchen Zhang, Mingsheng Long, Jianmin Wang, Michael I. Jordan
2020	J	`jnl`	On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. CoRR Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan
2020	J	`jnl`	On Thompson Sampling with Langevin Algorithms. CoRR Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	On the Adaptivity of Stochastic Gradient-Based Optimization. SIAM J. Optim. Lihua Lei, Michael I. Jordan
2020	A*	`conf`	On the Theory of Transfer Learning: The Importance of Task Diversity. NeurIPS Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
2020	J	`jnl`	On the Theory of Transfer Learning: The Importance of Task Diversity. CoRR Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
2020	J	`jnl`	Online Learning Demands in Max-min Fairness. CoRR Kirthevasan Kandasamy, Gur-Eyal Sela, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica
2020	J	`jnl`	Optimal Mean Estimation without a Variance. CoRR Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	Optimal Robust Linear Regression in Nearly Linear Time. CoRR Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett
2020	J	`jnl`	Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives. CoRR Michael Muehlebach, Michael I. Jordan
2020	A	`conf`	Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games. AAMAS Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry
2020	A	`conf`	Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. AISTATS Esther Rolf, Michael I. Jordan, Benjamin Recht
2020	J	`jnl`	Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. CoRR Esther Rolf, Michael I. Jordan, Benjamin Recht
2020	A*	`conf`	Projection Robust Wasserstein Distance and Riemannian Optimization. NeurIPS Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan
2020	J	`jnl`	Projection Robust Wasserstein Distance and Riemannian Optimization. CoRR Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan
2020	J	`jnl`	Provable Meta-Learning of Linear Representations. CoRR Nilesh Tripuraneni, Chi Jin, Michael I. Jordan
2020	A*	`conf`	Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. NeurIPS Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan
2020	A*	`conf`	Provably efficient reinforcement learning with linear function approximation. COLT Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
2020	J	`jnl`	ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. CoRR Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan
2020	J	`jnl`	Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism. CoRR Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez
2020	J	`jnl`	Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms. CoRR Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan
2020	A*	`conf`	Robust Optimization for Fairness with Noisy Protected Groups. NeurIPS Serena Lutong Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan
2020	J	`jnl`	Robust Optimization for Fairness with Noisy Protected Groups. CoRR Serena Lutong Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan
2020	J	`jnl`	Robustness Guarantees for Mode Estimation with an Application to Bandits. CoRR Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
2020	A	`conf`	Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models. AISTATS Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu
2020	J	`jnl`	Stochastic Approximation for Online Tensorial Independent Component Analysis. CoRR Chris Junchi Li, Michael I. Jordan
2020	A*	`conf`	Stochastic Gradient and Langevin Processes. ICML Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan
2020	A	`conf`	The Power of Batching in Multiple Hypothesis Testing. AISTATS Tijana Zrnic, Daniel L. Jiang, Aaditya Ramdas, Michael I. Jordan
2020	A*	`conf`	Transferable Calibration with Lower Bias and Variance in Domain Adaptation. NeurIPS Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan
2020	J	`jnl`	Transferable Calibration with Lower Bias and Variance in Domain Adaptation. CoRR Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan
2020	J	`jnl`	Uncertainty Sets for Image Classifiers using Conformal Prediction. CoRR Anastasios Angelopoulos, Stephen Bates, Jitendra Malik, Michael I. Jordan
2020	—	`conf`	Unsupervised Online Learning for Long-Term High Sensitivity Seizure Detection. EMBC Adelson Chua, Michael I. Jordan, Rikky Muller
2020	A*	`conf`	Variance Reduction With Sparse Gradients. ICLR Melih Elibol, Lihua Lei, Michael I. Jordan
2020	J	`jnl`	Variance Reduction with Sparse Gradients. CoRR Melih Elibol, Lihua Lei, Michael I. Jordan
2020	A*	`conf`	What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2019	J	`jnl`	A Control-Theoretic Perspective on Optimal High-Order Optimization. CoRR Tianyi Lin, Michael I. Jordan
2019	A*	`conf`	A Dynamical Systems Perspective on Nesterov Acceleration. ICML Michael Muehlebach, Michael I. Jordan
2019	J	`jnl`	A Dynamical Systems Perspective on Nesterov Acceleration. CoRR Michael Muehlebach, Michael I. Jordan
2019	J	`jnl`	A Higher-Order Swiss Army Infinitesimal Jackknife. CoRR Ryan Giordano, Michael I. Jordan, Tamara Broderick
2019	J	`jnl`	A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. CoRR Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2019	A	`conf`	A Swiss Army Infinitesimal Jackknife. AISTATS Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick
2019	J	`jnl`	A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. CoRR Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael I. Jordan, Nir Yosef
2019	J	`jnl`	Accelerated Primal-Dual Coordinate Descent for Computational Optimal Transport. CoRR Wenshuo Guo, Nhat Ho, Michael I. Jordan
2019	A*	`conf`	Acceleration via Symplectic Discretization of High-Resolution Differential Equations. NeurIPS Bin Shi, Simon S. Du, Weijie J. Su, Michael I. Jordan
2019	J	`jnl`	Acceleration via Symplectic Discretization of High-Resolution Differential Equations. CoRR Bin Shi, Simon S. Du, Weijie J. Su, Michael I. Jordan
2019	J	`jnl`	Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization. CoRR Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan
2019	J	`jnl`	Bayesian Robustness: A Nonasymptotic Viewpoint. CoRR Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, Michael I. Jordan
2019	J	`jnl`	Boundary Attack++: Query-Efficient Decision-Based Adversarial Attack. CoRR Jianbo Chen, Michael I. Jordan
2019	A*	`conf`	Bridging Theory and Algorithm for Domain Adaptation. ICML Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan
2019	J	`jnl`	Bridging Theory and Algorithm for Domain Adaptation. CoRR Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan
2019	J	`jnl`	Challenges with EM in application to weakly identifiable mixture models. CoRR Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu
2019	J	`jnl`	Competing Bandits in Matching Markets. CoRR Lydia T. Liu, Horia Mania, Michael I. Jordan
2019	J	`jnl`	Convergence Rates for Gaussian Mixtures of Experts. CoRR Nhat Ho, Chiao-Yu Yang, Michael I. Jordan
2019	J	`jnl`	Cost-Effective Incentive Allocation via Structured Counterfactual Inference. CoRR Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song
2019	J	`jnl`	Decoding From Pooled Data: Phase Transitions of Message Passing. IEEE Trans. Inf. Theory Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan
2019	J	`jnl`	Decoding from Pooled Data: Sharp Information-Theoretic Bounds. SIAM J. Math. Data Sci. Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan
2019	J	`jnl`	First-order methods almost always avoid strict saddle points. Math. Program. Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht
2019	J	`jnl`	Generalized Momentum-Based Methods: A Hamiltonian Perspective. CoRR Jelena Diakonikolas, Michael I. Jordan
2019	J	`jnl`	Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and 𝓁 CoRR Nhat Ho, Tianyi Lin, Michael I. Jordan
2019	J	`jnl`	High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. CoRR Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2019	J	`jnl`	Is There an Analog of Nesterov Acceleration for MCMC? CoRR Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan
2019	—	`conf`	L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data. ICLR (Poster) Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan
2019	J	`jnl`	LS-Tree: Model Interpretation When the Data Are Linguistic. CoRR Jianbo Chen, Michael I. Jordan
2019	J	`jnl`	Langevin Monte Carlo without Smoothness. CoRR Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett
2019	J	`jnl`	Learning Stages: Phenomenon, Root Cause, Mechanism Hypothesis, and Implications. CoRR Kaichao You, Mingsheng Long, Michael I. Jordan, Jianmin Wang
2019	J	`jnl`	ML-LOO: Detecting Adversarial Examples with Feature Attribution. CoRR Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan
2019	J	`jnl`	Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. CoRR Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2019	A*	`conf`	On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. ICML Tianyi Lin, Nhat Ho, Michael I. Jordan
2019	J	`jnl`	On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. CoRR Tianyi Lin, Nhat Ho, Michael I. Jordan
2019	J	`jnl`	On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games. CoRR Eric Mazumdar, Michael I. Jordan, S. Shankar Sastry
2019	J	`jnl`	On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. CoRR Tianyi Lin, Chi Jin, Michael I. Jordan
2019	J	`jnl`	On the Acceleration of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport. CoRR Tianyi Lin, Nhat Ho, Michael I. Jordan
2019	J	`jnl`	On the Adaptivity of Stochastic Gradient-Based Optimization. CoRR Lihua Lei, Michael I. Jordan
2019	J	`jnl`	On the Complexity of Approximating Multimarginal Optimal Transport. CoRR Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan
2019	J	`jnl`	Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings. CoRR Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry
2019	J	`jnl`	Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models. CoRR Chiao-Yu Yang, Nhat Ho, Michael I. Jordan
2019	A	`conf`	Probabilistic Multilevel Clustering via Composite Transportation Distance. AISTATS Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan
2019	J	`jnl`	Provably Efficient Reinforcement Learning with Linear Function Approximation. CoRR Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
2019	J	`jnl`	Quantitative Central Limit Theorems for Discrete Stochastic Processes. CoRR Xiang Cheng, Peter L. Bartlett, Michael I. Jordan
2019	J	`jnl`	Quantitative W CoRR Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan
2019	A*	`conf`	Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe
2019	J	`jnl`	Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing. CoRR Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2019	J	`jnl`	Stochastic Gradient Descent Escapes Saddle Points Efficiently. CoRR Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2019	J	`jnl`	SysML: The New Frontier of Machine Learning Systems. CoRR Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
2019	A*	`conf`	Theoretically Principled Trade-off between Robustness and Accuracy. ICML Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan
2019	J	`jnl`	Theoretically Principled Trade-off between Robustness and Accuracy. CoRR Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan
2019	A*	`conf`	Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. ICML Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan
2019	J	`jnl`	Towards Understanding the Transferability of Deep Representations. CoRR Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan
2019	A*	`conf`	Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers. ICML Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan
2019	A*	`conf`	Transferable Normalization: Towards Improving Transferability of Deep Neural Networks. NeurIPS Ximei Wang, Ying Jin, Mingsheng Long, Jianmin Wang, Michael I. Jordan
2019	J	`jnl`	Transferable Representation Learning with Deep Adaptation Networks. IEEE Trans. Pattern Anal. Mach. Intell. Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
2019	A*	`conf`	Universal Domain Adaptation. CVPR Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
2019	J	`jnl`	Wasserstein Reinforcement Learning. CoRR Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan
2018	J	`jnl`	A Deep Generative Model for Semi-Supervised Classification with Noisy Labels. CoRR Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael I. Jordan, Nir Yosef
2018	A*	`conf`	Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. COLT Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2018	J	`jnl`	Asynchronous Online Testing of Multiple Hypotheses. CoRR Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan
2018	A*	`conf`	Averaging Stochastic Gradient Descent on Riemannian Manifolds. COLT Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan
2018	J	`jnl`	Averaging Stochastic Gradient Descent on Riemannian Manifolds. CoRR Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan
2018	A*	`conf`	Conditional Adversarial Domain Adaptation. NeurIPS Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
2018	J	`jnl`	Covariances, Robustness, and Variational Bayes. J. Mach. Learn. Res. Ryan Giordano, Tamara Broderick, Michael I. Jordan
2018	A*	`conf`	Detection limits in the high-dimensional spiked rectangular model. COLT Ahmed El Alaoui, Michael I. Jordan
2018	J	`jnl`	Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation. CoRR Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan
2018	A*	`conf`	Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. NeurIPS Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan
2018	A*	`conf`	Generalized Zero-Shot Learning with Deep Calibration Network. NeurIPS Shichen Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan
2018	J	`jnl`	Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. CoRR Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan
2018	J	`jnl`	Improved Oracle Complexity for Stochastic Compositional Variance Reduced Gradient. CoRR Tianyi Lin, Chenyou Fan, Mengdi Wang, Michael I. Jordan
2018	A*	`conf`	Information Constraints on Auto-Encoding Variational Bayes. NeurIPS Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef
2018	J	`jnl`	Information Constraints on Auto-Encoding Variational Bayes. CoRR Romain Lopez, Jeffrey Regier, Nir Yosef, Michael I. Jordan
2018	A*	`conf`	Is Q-Learning Provably Efficient? NeurIPS Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan
2018	J	`jnl`	Is Q-learning Provably Efficient? CoRR Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan
2018	J	`jnl`	L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data. CoRR Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan
2018	A*	`conf`	Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. COLT Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht
2018	J	`jnl`	Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. CoRR Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht
2018	A*	`conf`	Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. ICML Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan
2018	J	`jnl`	Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. CoRR Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan
2018	—	`conf`	Machine learning: trends, perspectives and challenges. TURC Michael I. Jordan
2018	J	`jnl`	Minimizing Nonconvex Population Risk from Rough Empirical Risk. CoRR Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
2018	J	`jnl`	Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning. CoRR Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I. Jordan, Joseph E. Gonzalez, Sergey Levine
2018	J	`jnl`	Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. CoRR Nhat Ho, Tan M. Nguyen, Ankit B. Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk
2018	A*	`conf`	On the Local Minima of the Empirical Risk. NeurIPS Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
2018	A*	`conf`	On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. ICML Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
2018	J	`jnl`	On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. CoRR Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
2018	A*	`conf`	Partial Transfer Learning With Selective Adversarial Networks. CVPR Zhangjie Cao, Mingsheng Long, Jianmin Wang, Michael I. Jordan
2018	J	`jnl`	Probabilistic Multilevel Clustering via Composite Transportation Distance. CoRR Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan
2018	A*	`conf`	RLlib: Abstractions for Distributed Reinforcement Learning. ICML Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael I. Jordan, Ion Stoica
2018	J	`jnl`	Rao-Blackwellized Stochastic Gradients for Discrete Distributions. CoRR Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe
2018	A*	`conf`	Ray: A Distributed Framework for Emerging AI Applications. OSDI Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, Ion Stoica
2018	A*	`conf`	SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate. ICML Aaditya Ramdas, Tijana Zrnic, Martin J. Wainwright, Michael I. Jordan
2018	J	`jnl`	SAFFRON: an adaptive algorithm for online control of the false discovery rate. CoRR Aaditya Ramdas, Tijana Zrnic, Martin J. Wainwright, Michael I. Jordan
2018	J	`jnl`	Sampling Can Be Faster Than Optimization. CoRR Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan
2018	J	`jnl`	Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting. CoRR Xiang Cheng, Niladri S. Chatterji, Yasin Abbasi-Yadkori, Peter L. Bartlett, Michael I. Jordan
2018	A*	`conf`	Stochastic Cubic Regularization for Fast Nonconvex Optimization. NeurIPS Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
2018	A*	`conf`	Theoretical guarantees for EM under misspecified Gaussian mixture models. NeurIPS Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan
2018	A*	`conf`	Underdamped Langevin MCMC: A non-asymptotic analysis. COLT Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan
2018	J	`jnl`	Understanding the Acceleration Phenomenon via High-Resolution Differential Equations. CoRR Bin Shi, Simon S. Du, Michael I. Jordan, Weijie J. Su
2017	J	`jnl`	A Berkeley View of Systems Challenges for AI. CoRR Ion Stoica, Dawn Song, Raluca Ada Popa, David A. Patterson, Michael W. Mahoney, Randy H. Katz, Anthony D. Joseph, Michael I. Jordan, Joseph M. Hellerstein, Joseph E. Gonzalez, Ken Goldberg, Ali Ghodsi, David E. Culler, Pieter Abbeel
2017	J	`jnl`	A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin. IEEE Trans. Image Process. Sindhu Ghanta, Michael I. Jordan, Kivanç Köse, Dana H. Brooks, Milind Rajadhyaksha, Jennifer G. Dy
2017	J	`jnl`	A deep generative model for gene expression profiles from single-cell RNA sequencing. CoRR Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef
2017	J	`jnl`	Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. CoRR Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2017	A*	`conf`	Breaking Locality Accelerates Block Gauss-Seidel. ICML Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht
2017	J	`jnl`	CoCoA: A General Framework for Communication-Efficient Distributed Optimization. J. Mach. Learn. Res. Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi
2017	J	`jnl`	DAGGER: A sequential algorithm for FDR control on DAGs. CoRR Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan
2017	J	`jnl`	Decoding from Pooled Data: Phase Transitions of Message Passing. CoRR Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan
2017	B	`conf`	Decoding from pooled data: Phase transitions of message passing. ISIT Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan
2017	A*	`conf`	Deep Transfer Learning with Joint Adaptation Networks. ICML Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
2017	J	`jnl`	Distributed optimization with arbitrary local solvers. Optim. Methods Softw. Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
2017	J	`jnl`	Domain Adaptation with Randomized Multilinear Adversarial Networks. CoRR Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
2017	—	`conf`	Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. NIPS Jeffrey Regier, Michael I. Jordan, Jon McAuliffe
2017	J	`jnl`	Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. CoRR Jeffrey Regier, Michael I. Jordan, Jon McAuliffe
2017	J	`jnl`	Finite Size Corrections and Likelihood Ratio Fluctuations in the Spiked Wigner Model. CoRR Ahmed El Alaoui, Florent Krzakala, Michael I. Jordan
2017	J	`jnl`	First-order Methods Almost Always Avoid Saddle Points. CoRR Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht
2017	—	`conf`	Gradient Descent Can Take Exponential Time to Escape Saddle Points. NIPS Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabás Póczos
2017	J	`jnl`	Gradient Descent Can Take Exponential Time to Escape Saddle Points. CoRR Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Barnabás Póczos, Aarti Singh
2017	A*	`conf`	How to Escape Saddle Points Efficiently. ICML Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
2017	J	`jnl`	How to Escape Saddle Points Efficiently. CoRR Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
2017	—	`conf`	Kernel Feature Selection via Conditional Covariance Minimization. NIPS Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan
2017	J	`jnl`	Kernel Feature Selection via Conditional Covariance Minimization. CoRR Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan
2017	A	`conf`	Less than a Single Pass: Stochastically Controlled Stochastic Gradient. AISTATS Lihua Lei, Michael I. Jordan
2017	—	`conf`	Non-convex Finite-Sum Optimization Via SCSG Methods. NIPS Lihua Lei, Cheng Ju, Jianbo Chen, Michael I. Jordan
2017	J	`jnl`	Nonconvex Finite-Sum Optimization Via SCSG Methods. CoRR Lihua Lei, Cheng Ju, Jianbo Chen, Michael I. Jordan
2017	—	`conf`	On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic. SIGMETRICS (Abstracts) Michael I. Jordan
2017	A	`conf`	On the Learnability of Fully-Connected Neural Networks. AISTATS Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan
2017	—	`conf`	Online control of the false discovery rate with decaying memory. NIPS Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan
2017	J	`jnl`	Online control of the false discovery rate with decaying memory. CoRR Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan
2017	J	`jnl`	Partial Transfer Learning with Selective Adversarial Networks. CoRR Zhangjie Cao, Mingsheng Long, Jianmin Wang, Michael I. Jordan
2017	J	`jnl`	Perturbed Iterate Analysis for Asynchronous Stochastic Optimization. SIAM J. Optim. Horia Mania, Xinghao Pan, Dimitris S. Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan
2017	—	`conf`	QuTE: Decentralized multiple testing on sensor networks with false discovery rate control. CDC Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan
2017	J	`jnl`	Ray: A Distributed Framework for Emerging AI Applications. CoRR Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, William Paul, Michael I. Jordan, Ion Stoica
2017	A	`conf`	Real-Time Machine Learning: The Missing Pieces. HotOS Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael I. Jordan, Ion Stoica
2017	J	`jnl`	Real-Time Machine Learning: The Missing Pieces. CoRR Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael I. Jordan, Ion Stoica
2017	J	`jnl`	Saturating Splines and Feature Selection. J. Mach. Learn. Res. Nicholas Boyd, Trevor Hastie, Stephen P. Boyd, Benjamin Recht, Michael I. Jordan
2017	J	`jnl`	Stochastic Cubic Regularization for Fast Nonconvex Optimization. CoRR Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
2017	J	`jnl`	Underdamped Langevin MCMC: A non-asymptotic analysis. CoRR Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan
2016	A*	`conf`	A Kernelized Stein Discrepancy for Goodness-of-fit Tests. ICML Qiang Liu, Jason D. Lee, Michael I. Jordan
2016	A	`conf`	A Linearly-Convergent Stochastic L-BFGS Algorithm. AISTATS Philipp Moritz, Robert Nishihara, Michael I. Jordan
2016	J	`jnl`	A Lyapunov Analysis of Momentum Methods in Optimization. CoRR Ashia C. Wilson, Benjamin Recht, Michael I. Jordan
2016	J	`jnl`	A Variational Perspective on Accelerated Methods in Optimization. CoRR Andre Wibisono, Ashia C. Wilson, Michael I. Jordan
2016	J	`jnl`	Asymptotic behavior of ℓ CoRR Ahmed El Alaoui, Xiang Cheng, Aaditya Ramdas, Martin J. Wainwright, Michael I. Jordan
2016	J	`jnl`	CYCLADES: Conflict-free Asynchronous Machine Learning. CoRR Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris S. Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, Benjamin Recht
2016	J	`jnl`	CoCoA: A General Framework for Communication-Efficient Distributed Optimization. CoRR Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi
2016	J	`jnl`	Communication-efficient distributed statistical learning. CoRR Michael I. Jordan, Jason D. Lee, Yun Yang
2016	J	`jnl`	Decoding from Pooled Data: Sharp Information-Theoretic Bounds. CoRR Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan
2016	J	`jnl`	Deep Transfer Learning with Joint Adaptation Networks. CoRR Mingsheng Long, Jianmin Wang, Michael I. Jordan
2016	J	`jnl`	Function-Specific Mixing Times and Concentration Away from Equilibrium. CoRR Maxim Rabinovich, Aaditya Ramdas, Michael I. Jordan, Martin J. Wainwright
2016	J	`jnl`	Gradient Descent Converges to Minimizers. CoRR Jason D. Lee, Max Simchowitz, Michael I. Jordan, Benjamin Recht
2016	A*	`conf`	Gradient Descent Only Converges to Minimizers. COLT Jason D. Lee, Max Simchowitz, Michael I. Jordan, Benjamin Recht
2016	A*	`conf`	High-Dimensional Continuous Control Using Generalized Advantage Estimation. ICLR (Poster) John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel
2016	A*	`conf`	L1-regularized Neural Networks are Improperly Learnable in Polynomial Time. ICML Yuchen Zhang, Jason D. Lee, Michael I. Jordan
2016	J	`jnl`	Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method. CoRR Lihua Lei, Michael I. Jordan
2016	—	`conf`	Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. NIPS Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
2016	J	`jnl`	Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. CoRR Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
2016	J	`jnl`	Minimax Optimal Procedures for Locally Private Estimation. CoRR John C. Duchi, Martin J. Wainwright, Michael I. Jordan
2016	B	`conf`	On Computational Thinking, Inferential Thinking and Data Science. SPAA Michael I. Jordan
2016	A*	`conf`	SparkNet: Training Deep Networks in Spark. ICLR (Poster) Philipp Moritz, Robert Nishihara, Ion Stoica, Michael I. Jordan
2016	J	`jnl`	Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing. J. Mach. Learn. Res. Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan
2016	A*	`conf`	The Constrained Laplacian Rank Algorithm for Graph-Based Clustering. AAAI Feiping Nie, Xiaoqian Wang, Michael I. Jordan, Heng Huang
2016	J	`jnl`	Universality of Mallows' and degeneracy of Kendall's kernels for rankings. CoRR Horia Mania, Aaditya Ramdas, Martin J. Wainwright, Michael I. Jordan, Benjamin Recht
2016	—	`conf`	Unsupervised Domain Adaptation with Residual Transfer Networks. NIPS Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
2016	J	`jnl`	Unsupervised Domain Adaptation with Residual Transfer Networks. CoRR Mingsheng Long, Jianmin Wang, Michael I. Jordan
2015	A*	`conf`	A General Analysis of the Convergence of ADMM. ICML Robert Nishihara, Laurent Lessard, Benjamin Recht, Andrew K. Packard, Michael I. Jordan
2015	J	`jnl`	A General Analysis of the Convergence of ADMM. CoRR Robert Nishihara, Laurent Lessard, Benjamin Recht, Andrew K. Packard, Michael I. Jordan
2015	J	`jnl`	A Linearly-Convergent Stochastic L-BFGS Algorithm. CoRR Philipp Moritz, Robert Nishihara, Michael I. Jordan
2015	A*	`conf`	Adding vs. Averaging in Distributed Primal-Dual Optimization. ICML Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Adding vs. Averaging in Distributed Primal-Dual Optimization. CoRR Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Asynchronous Complex Analytics in a Distributed Dataflow Architecture. CoRR Joseph E. Gonzalez, Peter Bailis, Michael I. Jordan, Michael J. Franklin, Joseph M. Hellerstein, Ali Ghodsi, Ion Stoica
2015	—	`conf`	Automating model search for large scale machine learning. SoCC Evan Randall Sparks, Ameet Talwalkar, Daniel Haas, Michael J. Franklin, Michael I. Jordan, Tim Kraska
2015	J	`jnl`	Combinatorial Clustering and the Beta Negative Binomial Process. IEEE Trans. Pattern Anal. Mach. Intell. Tamara Broderick, Lester W. Mackey, John W. Paisley, Michael I. Jordan
2015	A*	`conf`	Computational Thinking, Inferential Thinking and "Big Data". PODS Michael I. Jordan
2015	A*	`conf`	Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds. ICML Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan
2015	J	`jnl`	Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds. CoRR Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan
2015	J	`jnl`	Distributed Optimization with Arbitrary Local Solvers. CoRR Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Distributed matrix completion and robust factorization. J. Mach. Learn. Res. Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan
2015	J	`jnl`	L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework. CoRR Virginia Smith, Simone Forte, Michael I. Jordan, Martin Jaggi
2015	J	`jnl`	Learning Halfspaces and Neural Networks with Random Initialization. CoRR Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan
2015	A*	`conf`	Learning Transferable Features with Deep Adaptation Networks. ICML Mingsheng Long, Yue Cao, Jianmin Wang, Michael I. Jordan
2015	—	`conf`	Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes. NIPS Ryan Giordano, Tamara Broderick, Michael I. Jordan
2015	—	`conf`	Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype? SIGMOD Conference Christopher Ré, Divy Agrawal, Magdalena Balazinska, Michael J. Cafarella, Michael I. Jordan, Tim Kraska, Raghu Ramakrishnan
2015	J	`jnl`	Nested Hierarchical Dirichlet Processes. IEEE Trans. Pattern Anal. Mach. Intell. John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan
2015	—	`conf`	On the Accuracy of Self-Normalized Log-Linear Models. NIPS Jacob Andreas, Maxim Rabinovich, Michael I. Jordan, Dan Klein
2015	J	`jnl`	On the Computational Complexity of High-Dimensional Bayesian Variable Selection. CoRR Yun Yang, Martin J. Wainwright, Michael I. Jordan
2015	J	`jnl`	On the accuracy of self-normalized log-linear models. CoRR Jacob Andreas, Maxim Rabinovich, Dan Klein, Michael I. Jordan
2015	J	`jnl`	Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations. IEEE Trans. Inf. Theory John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono
2015	A*	`conf`	Optimism-driven exploration for nonlinear systems. ICRA Teodor Mihai Moldovan, Sergey Levine, Michael I. Jordan, Pieter Abbeel
2015	—	`conf`	Parallel Correlation Clustering on Big Graphs. NIPS Xinghao Pan, Dimitris S. Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan
2015	J	`jnl`	Parallel Correlation Clustering on Big Graphs. CoRR Xinghao Pan, Dimitris S. Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan
2015	J	`jnl`	Perturbed Iterate Analysis for Asynchronous Stochastic Optimization. CoRR Horia Mania, Xinghao Pan, Dimitris S. Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan
2015	J	`jnl`	Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms. CoRR Yuchen Zhang, Michael I. Jordan
2015	A	`conf`	The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CIDR Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan
2015	A*	`conf`	Trust Region Policy Optimization. ICML John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, Philipp Moritz
2015	J	`jnl`	Trust Region Policy Optimization. CoRR John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel
2015	J	`jnl`	TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries. CoRR Evan Randall Sparks, Ameet Talwalkar, Michael J. Franklin, Michael I. Jordan, Tim Kraska
2015	—	`conf`	Variational Consensus Monte Carlo. NIPS Maxim Rabinovich, Elaine Angelino, Michael I. Jordan
2015	J	`jnl`	ℓ CoRR Yuchen Zhang, Jason D. Lee, Michael I. Jordan
2014	—	`ch.`	Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference. Handbook of Mixed Membership Models and Their Applications John W. Paisley, David M. Blei, Michael I. Jordan
2014	B	`conf`	Changepoint Analysis for Efficient Variant Calling. RECOMB Adam E. Bloniarz, Ameet Talwalkar, Jonathan Terhorst, Michael I. Jordan, David A. Patterson, Bin Yu, Yun S. Song
2014	—	`conf`	Communication-Efficient Distributed Dual Coordinate Ascent. NIPS Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
2014	J	`jnl`	Communication-Efficient Distributed Dual Coordinate Ascent. CoRR Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
2014	J	`jnl`	Information-theoretic lower bounds for distributed statistical estimation with communication constraints. CoRR John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Yuchen Zhang
2014	J	`jnl`	Iterative Discovery of Multiple AlternativeClustering Views. IEEE Trans. Pattern Anal. Mach. Intell. Donglin Niu, Jennifer G. Dy, Michael I. Jordan
2014	—	`conf`	Knowing when you're wrong: building fast and reliable approximate query processing systems. SIGMOD Conference Sameer Agarwal, Henry Milner, Ariel Kleiner, Ameet Talwalkar, Michael I. Jordan, Samuel Madden, Barzan Mozafari, Ion Stoica
2014	A*	`conf`	Lower bounds on the performance of polynomial-time algorithms for sparse linear regression. COLT Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan
2014	J	`jnl`	Lower bounds on the performance of polynomial-time algorithms for sparse linear regression. CoRR Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan
2014	—	`ch.`	Mixed Membership Matrix Factorization. Handbook of Mixed Membership Models and Their Applications Lester W. Mackey, David J. Weiss, Michael I. Jordan
2014	—	`ch.`	Mixed Membership Models for Time Series. Handbook of Mixed Membership Models and Their Applications Emily B. Fox, Michael I. Jordan
2014	—	`ch.`	Neural Networks. Computing Handbook, 3rd ed. (1) Michael I. Jordan, Christopher M. Bishop
2014	—	`conf`	On the Convergence Rate of Decomposable Submodular Function Minimization. NIPS Robert Nishihara, Stefanie Jegelka, Michael I. Jordan
2014	J	`jnl`	On the Convergence Rate of Decomposable Submodular Function Minimization. CoRR Robert Nishihara, Stefanie Jegelka, Michael I. Jordan
2014	—	`conf`	Parallel Double Greedy Submodular Maximization. NIPS Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, Michael I. Jordan
2014	J	`jnl`	Particle gibbs with ancestor sampling. J. Mach. Learn. Res. Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
2014	J	`jnl`	Privacy Aware Learning. J. ACM John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2014	J	`jnl`	SMaSH: a benchmarking toolkit for human genome variant calling. Bioinform. Ameet Talwalkar, Jesse Liptrap, Julie Newcomb, Christopher Hartl, Jonathan Terhorst, Kristal Curtis, Ma'ayan Bresler, Yun S. Song, Michael I. Jordan, David A. Patterson
2014	J	`jnl`	Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning. Proc. VLDB Endow. Barzan Mozafari, Purnamrita Sarkar, Michael J. Franklin, Michael I. Jordan, Samuel Madden
2014	—	`conf`	Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing. NIPS Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan
2014	J	`jnl`	The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CoRR Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan
2013	—	`conf`	A Comparative Framework for Preconditioned Lasso Algorithms. NIPS Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan
2013	—	`conf`	A Nested HDP for Hierarchical Topic Models ICLR (Workshop) John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan
2013	A*	`conf`	A general bootstrap performance diagnostic. KDD Ariel Kleiner, Ameet Talwalkar, Sameer Agarwal, Ion Stoica, Michael I. Jordan
2013	J	`jnl`	Bayesian semiparametric Wiener system identification. Autom. Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan
2013	J	`jnl`	Cluster Forests. Comput. Stat. Data Anal. Donghui Yan, Aiyou Chen, Michael I. Jordan
2013	J	`jnl`	Computing Upper and Lower Bounds on Likelihoods in Intractable Networks CoRR Tommi S. Jaakkola, Michael I. Jordan
2013	A*	`conf`	Distributed Low-Rank Subspace Segmentation. ICCV Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan
2013	J	`jnl`	Divide-and-Conquer Subspace Segmentation CoRR Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan
2013	—	`conf`	Efficient Ranking from Pairwise Comparisons. ICML (3) Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic
2013	J	`jnl`	Efficient Stepwise Selection in Decomposable Models CoRR Amol Deshpande, Minos N. Garofalakis, Michael I. Jordan
2013	—	`conf`	Estimation, Optimization, and Parallelism when Data is Sparse. NIPS John C. Duchi, Michael I. Jordan, H. Brendan McMahan
2013	—	`conf`	Information-theoretic lower bounds for distributed statistical estimation with communication constraints. NIPS Yuchen Zhang, John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2013	J	`jnl`	Learning Dependency-Based Compositional Semantics. Comput. Linguistics Percy Liang, Michael I. Jordan, Dan Klein
2013	J	`jnl`	Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation CoRR John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2013	—	`conf`	Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation. NIPS John C. Duchi, Martin J. Wainwright, Michael I. Jordan
2013	J	`jnl`	Local Privacy and Statistical Minimax Rates CoRR John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2013	A*	`conf`	Local Privacy and Statistical Minimax Rates. FOCS John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2013	—	`conf`	Local privacy and statistical minimax rates. Allerton John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2013	J	`jnl`	Loopy Belief Propagation for Approximate Inference: An Empirical Study CoRR Kevin P. Murphy, Yair Weiss, Michael I. Jordan
2013	J	`jnl`	Loopy Belief Propogation and Gibbs Measures CoRR Sekhar Tatikonda, Michael I. Jordan
2013	—	`conf`	MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. ICML (3) Tamara Broderick, Brian Kulis, Michael I. Jordan
2013	A*	`conf`	MLI: An API for Distributed Machine Learning. ICDM Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska
2013	J	`jnl`	MLI: An API for Distributed Machine Learning. CoRR Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska
2013	A	`conf`	MLbase: A Distributed Machine-learning System. CIDR Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan
2013	J	`jnl`	Mixed Membership Models for Time Series. CoRR Emily B. Fox, Michael I. Jordan
2013	J	`jnl`	Mixture Representations for Inference and Learning in Boltzmann Machines CoRR Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan
2013	J	`jnl`	On statistics, computation and scalability. CoRR Michael I. Jordan
2013	J	`jnl`	Optimal rates for zero-order optimization: the power of two function evaluations. CoRR John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono
2013	—	`conf`	Optimistic Concurrency Control for Distributed Unsupervised Learning. NIPS Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan
2013	J	`jnl`	Optimistic Concurrency Control for Distributed Unsupervised Learning. CoRR Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan
2013	J	`jnl`	PEGASUS: A Policy Search Method for Large MDPs and POMDPs CoRR Andrew Y. Ng, Michael I. Jordan
2013	—	`conf`	Streaming Variational Bayes. NIPS Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan
2013	J	`jnl`	Streaming Variational Bayes. CoRR Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan
2013	J	`jnl`	Tree-dependent Component Analysis CoRR Francis R. Bach, Michael I. Jordan
2013	J	`jnl`	Variational MCMC CoRR Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Michael I. Jordan, Stuart Russell
2012	J	`jnl`	A Generalized Mean Field Algorithm for Variational Inference in Exponential Families CoRR Eric P. Xing, Michael I. Jordan, Stuart Russell
2012	J	`jnl`	Active Learning for Crowd-Sourced Databases CoRR Barzan Mozafari, Purnamrita Sarkar, Michael J. Franklin, Michael I. Jordan, Samuel Madden
2012	A*	`conf`	Active spectral clustering via iterative uncertainty reduction. KDD Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan
2012	—	`conf`	Ancestor Sampling for Particle Gibbs. NIPS Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
2012	J	`jnl`	Bayesian Multicategory Support Vector Machines. CoRR Zhihua Zhang, Michael I. Jordan
2012	J	`jnl`	Coherence functions with applications in large-margin classification methods. J. Mach. Learn. Res. Zhihua Zhang, Dehua Liu, Guang Dai, Michael I. Jordan
2012	J	`jnl`	Computational and Statistical Tradeoffs via Convex Relaxation CoRR Venkat Chandrasekaran, Michael I. Jordan
2012	A*	`conf`	Divide-and-conquer and statistical inference for big data. KDD Michael I. Jordan
2012	J	`jnl`	EP-GIG Priors and Applications in Bayesian Sparse Learning. J. Mach. Learn. Res. Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan
2012	J	`jnl`	Ergodic Mirror Descent. SIAM J. Optim. John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan
2012	—	`conf`	Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods. NIPS John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono
2012	J	`jnl`	Graph partition strategies for generalized mean field inference CoRR Eric P. Xing, Michael I. Jordan, Stuart Russell
2012	J	`jnl`	Modeling Events with Cascades of Poisson Processes CoRR Aleksandr Simma, Michael I. Jordan
2012	J	`jnl`	Nested Hierarchical Dirichlet Processes CoRR John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan
2012	A*	`conf`	Nonparametric Link Prediction in Dynamic Networks. ICML Purnamrita Sarkar, Deepayan Chakrabarti, Michael I. Jordan
2012	J	`jnl`	Optimization of Structured Mean Field Objectives CoRR Alexandre Bouchard-Côté, Michael I. Jordan
2012	J	`jnl`	Privacy Aware Learning CoRR John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2012	—	`conf`	Privacy Aware Learning. NIPS John C. Duchi, Michael I. Jordan, Martin J. Wainwright
2012	J	`jnl`	Qualcomm Context-Awareness Symposium Sets Research Agenda for Context-Aware Smartphones. IEEE Pervasive Comput. Paul Lukowicz, Sanjiv Nanda, Vidya Narayanan, Hal Abelson, Deborah L. McGuinness, Michael I. Jordan
2012	A*	`conf`	Revisiting k-means: New Algorithms via Bayesian Nonparametrics. ICML Brian Kulis, Michael I. Jordan
2012	—	`conf`	Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. NIPS Ke Jiang, Brian Kulis, Michael I. Jordan
2012	A	`conf`	Stick-Breaking Beta Processes and the Poisson Process. AISTATS John W. Paisley, David M. Blei, Michael I. Jordan
2012	J	`jnl`	The Asymptotics of Ranking Algorithms CoRR John C. Duchi, Lester W. Mackey, Michael I. Jordan
2012	A*	`conf`	The Big Data Bootstrap. ICML Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan
2012	J	`jnl`	The DLR Hierarchy of Approximate Inference CoRR Michal Rosen-Zvi, Michael I. Jordan, Alan L. Yuille
2012	J	`jnl`	The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features CoRR Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan
2012	A*	`conf`	Variational Bayesian Inference with Stochastic Search. ICML John W. Paisley, David M. Blei, Michael I. Jordan
2011	A*	`conf`	A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection. ICML Yue Guan, Jennifer G. Dy, Michael I. Jordan
2011	—	`conf`	Bayesian Bias Mitigation for Crowdsourcing. NIPS Fabian L. Wauthier, Michael I. Jordan
2011	J	`jnl`	Bayesian Generalized Kernel Mixed Models. J. Mach. Learn. Res. Zhihua Zhang, Guang Dai, Michael I. Jordan
2011	J	`jnl`	Bayesian Nonparametric Inference of Switching Dynamic Linear Models. IEEE Trans. Signal Process. Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
2011	J	`jnl`	Cluster Forests CoRR Donghui Yan, Aiyou Chen, Michael I. Jordan
2011	A	`conf`	Dimensionality Reduction for Spectral Clustering. AISTATS Donglin Niu, Jennifer G. Dy, Michael I. Jordan
2011	J	`jnl`	Divide-and-Conquer Matrix Factorization CoRR Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan
2011	—	`conf`	Divide-and-Conquer Matrix Factorization. NIPS Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan
2011	—	`conf`	Ergodic mirror descent. Allerton John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan
2011	J	`jnl`	Learning Dependency-Based Compositional Semantics CoRR Percy Liang, Michael I. Jordan, Dan Klein
2011	A*	`conf`	Learning Dependency-Based Compositional Semantics. ACL Percy Liang, Michael I. Jordan, Dan Klein
2011	J	`jnl`	Learning Low-Dimensional Signal Models. IEEE Signal Process. Mag. Lawrence Carin, Richard G. Baraniuk, Volkan Cevher, David B. Dunson, Michael I. Jordan, Guillermo Sapiro, Michael B. Wakin
2011	A*	`conf`	Managing data transfers in computer clusters with orchestra. SIGCOMM Mosharaf Chowdhury, Matei Zaharia, Justin Ma, Michael I. Jordan, Ion Stoica
2011	J	`jnl`	Non-parametric Link Prediction CoRR Purnamrita Sarkar, Deepayan Chakrabarti, Michael I. Jordan
2011	A	`conf`	Nonparametric Bayesian Co-clustering Ensembles. SDM Pu Wang, Kathryn B. Laskey, Carlotta Domeniconi, Michael I. Jordan
2011	B	`conf`	Nonparametric Combinatorial Sequence Models. RECOMB Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic
2011	J	`jnl`	Nonparametric Combinatorial Sequence Models. J. Comput. Biol. Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic
2011	J	`jnl`	Revisiting k-means: New Algorithms via Bayesian Nonparametrics CoRR Brian Kulis, Michael I. Jordan
2011	A*	`conf`	Supervised hierarchical Pitman-Yor process for natural scene segmentation. CVPR Alex Shyr, Trevor Darrell, Michael I. Jordan, Raquel Urtasun
2011	A	`conf`	The SCADS Director: Scaling a Distributed Storage System Under Stringent Performance Requirements. FAST Beth Trushkowsky, Peter Bodík, Armando Fox, Michael J. Franklin, Michael I. Jordan, David A. Patterson
2011	J	`jnl`	Variational Probabilistic Inference and the QMR-DT Network CoRR Tommi S. Jaakkola, Michael I. Jordan
2011	—	`conf`	Visually Relating Gene Expression and in vivo DNA Binding Data. BIBM Min-Yu Huang, Lester W. Mackey, Soile V. E. Keränen, Gunther H. Weber, Michael I. Jordan, David W. Knowles, Mark D. Biggin, Bernd Hamann
2010	J	`jnl`	Active site prediction using evolutionary and structural information. Bioinform. Sriram Sankararaman, Fei Sha, Jack F. Kirsch, Michael I. Jordan, Kimmen Sjölander
2010	A*	`conf`	An Analysis of the Convergence of Graph Laplacians. ICML Daniel Ting, Ling Huang, Michael I. Jordan
2010	A	`conf`	Bayesian Generalized Kernel Models. AISTATS Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan
2010	J	`jnl`	Bayesian Inference in Queueing Networks CoRR Charles Sutton, Michael I. Jordan
2010	J	`jnl`	Bayesian Nonparametric Methods for Learning Markov Switching Processes. IEEE Signal Process. Mag. Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
2010	—	`conf`	Characterizing, modeling, and generating workload spikes for stateful services. SoCC Peter Bodík, Armando Fox, Michael J. Franklin, Michael I. Jordan, David A. Patterson
2010	J	`jnl`	Convex and Semi-Nonnegative Matrix Factorizations. IEEE Trans. Pattern Anal. Mach. Intell. Chris H. Q. Ding, Tao Li, Michael I. Jordan
2010	A*	`conf`	Detecting Large-Scale System Problems by Mining Console Logs. ICML Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan
2010	J	`jnl`	Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization. IEEE Trans. Inf. Theory XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2010	—	`conf`	Experience Mining Google's Production Console Logs. SLAML Wei Xu, Ling Huang, Michael I. Jordan
2010	—	`conf`	Heavy-Tailed Process Priors for Selective Shrinkage. NIPS Fabian L. Wauthier, Michael I. Jordan
2010	A	`conf`	Inference and Learning in Networks of Queues. AISTATS Charles Sutton, Michael I. Jordan
2010	J	`jnl`	Joint covariate selection and joint subspace selection for multiple classification problems. Stat. Comput. Guillaume Obozinski, Ben Taskar, Michael I. Jordan
2010	A*	`conf`	Learning Programs: A Hierarchical Bayesian Approach. ICML Percy Liang, Michael I. Jordan, Dan Klein
2010	A	`conf`	Matrix-Variate Dirichlet Process Mixture Models. AISTATS Zhihua Zhang, Guang Dai, Michael I. Jordan
2010	A*	`conf`	Mixed Membership Matrix Factorization. ICML Lester W. Mackey, David J. Weiss, Michael I. Jordan
2010	A	`conf`	Modeling Events with Cascades of Poisson Processes. UAI Aleksandr Simma, Michael I. Jordan
2010	A*	`conf`	Multiple Non-Redundant Spectral Clustering Views. ICML Donglin Niu, Jennifer G. Dy, Michael I. Jordan
2010	J	`jnl`	Neighbor-Dependent Ramachandran Probability Distributions of Amino Acids Developed from a Hierarchical Dirichlet Process Model. PLoS Comput. Biol. Daniel Ting, Guoli Wang, Maxim V. Shapovalov, Rajib Mitra, Michael I. Jordan, Roland L. Dunbrack Jr.
2010	A*	`conf`	On the Consistency of Ranking Algorithms. ICML John C. Duchi, Lester W. Mackey, Michael I. Jordan
2010	—	`conf`	Random Conic Pursuit for Semidefinite Programming. NIPS Ariel Kleiner, Ali Rahimi, Michael I. Jordan
2010	J	`jnl`	Regularized Discriminant Analysis, Ridge Regression and Beyond. J. Mach. Learn. Res. Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan
2010	A*	`conf`	Sufficient dimension reduction for visual sequence classification. CVPR Alex Shyr, Raquel Urtasun, Michael I. Jordan
2010	J	`jnl`	The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. J. ACM David M. Blei, Thomas L. Griffiths, Michael I. Jordan
2010	—	`conf`	Tree-Structured Stick Breaking for Hierarchical Data. NIPS Ryan Prescott Adams, Zoubin Ghahramani, Michael I. Jordan
2010	—	`conf`	Type-Based MCMC. HLT-NAACL Percy Liang, Michael I. Jordan, Dan Klein
2010	—	`conf`	Unsupervised Kernel Dimension Reduction. NIPS Meihong Wang, Fei Sha, Michael I. Jordan
2010	—	`conf`	Variational Inference over Combinatorial Spaces. NIPS Alexandre Bouchard-Côté, Michael I. Jordan
2009	—	`conf`	A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. ECML/PKDD (2) Zhihua Zhang, Guang Dai, Michael I. Jordan
2009	—	`conf`	Asymptotically Optimal Regularization in Smooth Parametric Models. NIPS Percy Liang, Francis R. Bach, Guillaume Bouchard, Michael I. Jordan
2009	—	`conf`	Automatic exploration of datacenter performance regimes. ACDC@ICAC Peter Bodík, Rean Griffith, Charles Sutton, Armando Fox, Michael I. Jordan, David A. Patterson
2009	A	`conf`	Coherence Functions for Multicategory Margin-based Classification Methods. AISTATS Zhihua Zhang, Michael I. Jordan, Wu-Jun Li, Dit-Yan Yeung
2009	A*	`conf`	Combinatorial stochastic processes and nonparametric Bayesian modeling. SODA Michael I. Jordan
2009	A*	`conf`	Detecting large-scale system problems by mining console logs. SOSP Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan
2009	A*	`conf`	Fast approximate spectral clustering. KDD Donghui Yan, Ling Huang, Michael I. Jordan
2009	J	`jnl`	Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data. Bioinform. Junming Yin, Michael I. Jordan, Yun S. Song
2009	A	`conf`	Latent Variable Models for Dimensionality Reduction. AISTATS Zhihua Zhang, Michael I. Jordan
2009	—	`conf`	Learning Semantic Correspondences with Less Supervision. ACL/IJCNLP Percy Liang, Michael I. Jordan, Dan Klein
2009	A*	`conf`	Learning from measurements in exponential families. ICML Percy Liang, Michael I. Jordan, Dan Klein
2009	—	`conf`	Nonparametric Latent Feature Models for Link Prediction. NIPS Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan
2009	A*	`conf`	Online System Problem Detection by Mining Patterns of Console Logs. ICDM Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan
2009	A	`conf`	Optimization of Structured Mean Field Objectives. UAI Alexandre Bouchard-Côté, Michael I. Jordan
2009	A*	`conf`	Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning. ICDE Archana Ganapathi, Harumi A. Kuno, Umeshwar Dayal, Janet L. Wiener, Armando Fox, Michael I. Jordan, David A. Patterson
2009	—	`conf`	Sharing Features among Dynamical Systems with Beta Processes. NIPS Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
2009	—	`conf`	Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters. HotCloud Peter Bodík, Rean Griffith, Charles Sutton, Armando Fox, Michael I. Jordan, David A. Patterson
2008	J	`jnl`	A Dual Receptor Crosstalk Model of G-Protein-Coupled Signal Transduction. PLoS Comput. Biol. Patrick Flaherty, Mala L. Radhakrishnan, Tuan Dinh, Robert A. Rebres, Tamara I. Roach, Michael I. Jordan, Adam P. Arkin
2008	A*	`conf`	An HDP-HMM for systems with state persistence. ICML Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
2008	A*	`conf`	An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. ICML Percy Liang, Michael I. Jordan
2008	—	`conf`	DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. NIPS Simon Lacoste-Julien, Fei Sha, Michael I. Jordan
2008	—	`conf`	Efficient Inference in Phylogenetic InDel Trees. NIPS Alexandre Bouchard-Côté, Michael I. Jordan, Dan Klein
2008	J	`jnl`	Estimating divergence functionals and the likelihood ratio by convex risk minimization CoRR XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2008	J	`jnl`	Graphical Models, Exponential Families, and Variational Inference. Found. Trends Mach. Learn. Martin J. Wainwright, Michael I. Jordan
2008	—	`conf`	High-dimensional support union recovery in multivariate regression. NIPS Guillaume Obozinski, Martin J. Wainwright, Michael I. Jordan
2008	—	`conf`	Mining Console Logs for Large-Scale System Problem Detection. SysML Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan
2008	A*	`conf`	Nonnegative Matrix Factorization for Combinatorial Optimization: Spectral Clustering, Graph Matching, and Clique Finding. ICDM Chris H. Q. Ding, Tao Li, Michael I. Jordan
2008	—	`conf`	Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. NIPS Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
2008	J	`jnl`	On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection. IEEE Trans. Inf. Theory XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2008	B	`conf`	On the Inference of Ancestries in Admixed Populations. RECOMB Sriram Sankararaman, Gad Kimmel, Eran Halperin, Michael I. Jordan
2008	—	`conf`	Posterior Consistency of the Silverman g-prior in Bayesian Model Choice. NIPS Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung
2008	—	`conf`	Probabilistic Inference in Queueing Networks. SysML Charles Sutton, Michael I. Jordan
2008	—	`conf`	Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. NIPS Erik B. Sudderth, Michael I. Jordan
2008	—	`conf`	Spectral Clustering with Perturbed Data. NIPS Ling Huang, Donghui Yan, Michael I. Jordan, Nina Taft
2008	A	`conf`	The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. UAI Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan
2008	—	`conf`	Union support recovery in high-dimensional multivariate regression. Allerton Guillaume Obozinski, Martin J. Wainwright, Michael I. Jordan
2007	J	`jnl`	A Direct Formulation for Sparse PCA Using Semidefinite Programming. SIAM Rev. Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet
2007	A*	`conf`	A permutation-augmented sampler for DP mixture models. ICML Percy Liang, Michael I. Jordan, Benjamin Taskar
2007	—	`conf`	Agreement-Based Learning. NIPS Percy Liang, Dan Klein, Michael I. Jordan
2007	J	`jnl`	Bayesian Haplotype Inference via the Dirichlet Process. J. Comput. Biol. Eric P. Xing, Michael I. Jordan, Roded Sharan
2007	A*	`conf`	Communication-Efficient Online Detection of Network-Wide Anomalies. INFOCOM Ling Huang, XuanLong Nguyen, Minos N. Garofalakis, Joseph M. Hellerstein, Michael I. Jordan, Anthony D. Joseph, Nina Taft
2007	—	`conf`	Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization. NIPS XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2007	—	`conf`	Feature Selection Methods for Improving Protein Structure Prediction with Rosetta. NIPS Ben Blum, Michael I. Jordan, David E. Kim, Rhiju Das, Philip Bradley, David Baker
2007	A	`conf`	Hierarchical Beta Processes and the Indian Buffet Process. AISTATS Romain Thibaux, Michael I. Jordan
2007	—	`conf`	Image Denoising with Nonparametric Hidden Markov Trees. ICIP (3) Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan
2007	A*	`conf`	Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. ICCV Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan
2007	B	`conf`	Nonparametric estimation of the likelihood ratio and divergence functionals. ISIT XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2007	A*	`conf`	Regression on manifolds using kernel dimension reduction. ICML Jens Nilsson, Fei Sha, Michael I. Jordan
2007	A*	`conf`	Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization. ICDM Tao Li, Chris H. Q. Ding, Michael I. Jordan
2007	—	`conf`	Statistical Machine Learning and Computational Biology. BIBM Michael I. Jordan
2007	—	`conf`	The Infinite PCFG Using Hierarchical Dirichlet Processes. EMNLP-CoNLL Percy Liang, Slav Petrov, Michael I. Jordan, Dan Klein
2006	A*	`conf`	A graphical model for predicting protein molecular function. ICML Barbara E. Engelhardt, Michael I. Jordan, Steven E. Brenner
2006	A	`conf`	Bayesian Multicategory Support Vector Machines. UAI Zhihua Zhang, Michael I. Jordan
2006	A*	`conf`	Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture. ICML Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh
2006	—	`ch.`	Gaussian Processes and the Null-Category Noise Model. Semi-Supervised Learning Neil D. Lawrence, Michael I. Jordan
2006	—	`conf`	In-Network PCA and Anomaly Detection. NIPS Ling Huang, XuanLong Nguyen, Minos N. Garofalakis, Michael I. Jordan, Anthony D. Joseph, Nina Taft
2006	J	`jnl`	Learning Spectral Clustering, With Application To Speech Separation. J. Mach. Learn. Res. Francis R. Bach, Michael I. Jordan
2006	J	`jnl`	Log-determinant relaxation for approximate inference in discrete Markov random fields. IEEE Trans. Signal Process. Martin J. Wainwright, Michael I. Jordan
2006	J	`jnl`	Nonparametric empirical Bayes for the Dirichlet process mixture model. Stat. Comput. Jon D. McAuliffe, David M. Blei, Michael I. Jordan
2006	B	`conf`	On optimal quantization rules for sequential decision problems. ISIT XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2006	J	`jnl`	On optimal quantization rules for some sequential decision problems CoRR XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2006	A*	`conf`	Statistical debugging: simultaneous identification of multiple bugs. ICML Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken
2006	J	`jnl`	Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span. BMC Bioinform. David M. Blei, K. Franks, Michael I. Jordan, I. Saira Mian
2006	J	`jnl`	Structured Prediction, Dual Extragradient and Bregman Projections. J. Mach. Learn. Res. Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan
2006	—	`conf`	Word Alignment via Quadratic Assignment. HLT-NAACL Simon Lacoste-Julien, Benjamin Taskar, Dan Klein, Michael I. Jordan
2005	J	`jnl`	A kernel-based learning approach to ad hoc sensor network localization. ACM Trans. Sens. Networks XuanLong Nguyen, Michael I. Jordan, Bruno Sinopoli
2005	J	`jnl`	A latent variable model for chemogenomic profiling. Bioinform. Patrick Flaherty, Guri Giaever, Jochen Kumm, Michael I. Jordan, Adam P. Arkin
2005	—	`conf`	Combining Visualization and Statistical Analysis to Improve Operator Confidence and Efficiency for Failure Detection and Localization. ICAC Peter Bodík, Greg Friedman, Lukas Biewald, Helen Levine, George Candea, Kayur Patel, Gilman Tolle, Jonathan Hui, Armando Fox, Michael I. Jordan, David A. Patterson
2005	—	`conf`	Discriminative training of hidden Markov models for multiple pitch tracking [speech processing examples]. ICASSP (5) Francis R. Bach, Michael I. Jordan
2005	—	`conf`	Divergences, surrogate loss functions and experimental design. NIPS XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2005	—	`conf`	Modèles de Markov cachés pour l'estimation de plusieurs fréquences fondamentales. EGC (Ateliers) Michael I. Jordan, Francis R. Bach
2005	—	`conf`	Multi-instrument musical transcription using a dynamic graphical model. ICASSP (5) Brian K. Vogel, Michael I. Jordan, David Wessel
2005	J	`jnl`	Nonparametric decentralized detection using kernel methods. IEEE Trans. Signal Process. XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2005	J	`jnl`	On divergences, surrogate loss functions, and decentralized detection CoRR XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2005	A*	`conf`	Predictive low-rank decomposition for kernel methods. ICML Francis R. Bach, Michael I. Jordan
2005	J	`jnl`	Protein Molecular Function Prediction by Bayesian Phylogenomics. PLoS Comput. Biol. Barbara E. Engelhardt, Michael I. Jordan, Kathryn E. Muratore, Steven E. Brenner
2005	—	`conf`	Robust design of biological experiments. NIPS Patrick Flaherty, Michael I. Jordan, Adam P. Arkin
2005	A*	`conf`	Scalable statistical bug isolation. PLDI Ben Liblit, Mayur Naik, Alice X. Zheng, Alex Aiken, Michael I. Jordan
2005	A	`conf`	Semiparametric latent factor models. AISTATS Yee Whye Teh, Matthias W. Seeger, Michael I. Jordan
2005	—	`conf`	Structured Prediction via the Extragradient Method. NIPS Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan
2005	A	`conf`	The DLR Hierarchy of Approximate Inference. UAI Michal Rosen-Zvi, Michael I. Jordan, Alan L. Yuille
2004	—	`conf`	A Direct Formulation for Sparse PCA Using Semidefinite Programming. NIPS Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet
2004	J	`jnl`	A direct formulation for sparse PCA using semidefinite programming CoRR Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet
2004	J	`jnl`	A statistical framework for genomic data fusion. Bioinform. Gert R. G. Lanckriet, Tijl De Bie, Nello Cristianini, Michael I. Jordan, William Stafford Noble
2004	A*	`conf`	Bayesian haplo-type inference via the dirichlet process. ICML Eric P. Xing, Roded Sharan, Michael I. Jordan
2004	—	`conf`	Blind One-microphone Speech Separation: A Spectral Learning Approach. NIPS Francis R. Bach, Michael I. Jordan
2004	—	`conf`	Computing regularization paths for learning multiple kernels. NIPS Francis R. Bach, Romain Thibaux, Michael I. Jordan
2004	A*	`conf`	Decentralized detection and classification using kernel methods. ICML XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
2004	J	`jnl`	Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces. J. Mach. Learn. Res. Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
2004	—	`conf`	Extensions of the Informative Vector Machine. Deterministic and Statistical Methods in Machine Learning Neil D. Lawrence, John C. Platt, Michael I. Jordan
2004	—	`conf`	Failure Diagnosis Using Decision Trees. ICAC Mike Y. Chen, Alice X. Zheng, Jim Lloyd, Michael I. Jordan, Eric A. Brewer
2004	A	`conf`	Graph Partition Strategies for Generalized Mean Field Inference. UAI Eric P. Xing, Michael I. Jordan
2004	J	`jnl`	Kalman filtering with intermittent observations. IEEE Trans. Autom. Control. Bruno Sinopoli, Luca Schenato, Massimo Franceschetti, Kameshwar Poolla, Michael I. Jordan, Shankar S. Sastry
2004	Misc	`conf`	Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast. Pacific Symposium on Biocomputing Gert R. G. Lanckriet, Minghua Deng, Nello Cristianini, Michael I. Jordan, William Stafford Noble
2004	J	`jnl`	Learning graphical models for stationary time series. IEEE Trans. Signal Process. Francis R. Bach, Michael I. Jordan
2004	J	`jnl`	Learning the Kernel Matrix with Semidefinite Programming. J. Mach. Learn. Res. Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan
2004	J	`jnl`	Logos: a Modular Bayesian Model for J. Bioinform. Comput. Biol. Eric P. Xing, Wei Wu, Michael I. Jordan, Richard M. Karp
2004	A*	`conf`	Multiple kernel learning, conic duality, and the SMO algorithm. ICML Francis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan
2004	J	`jnl`	Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. Bioinform. Jon D. McAuliffe, Lior Pachter, Michael I. Jordan
2004	J	`jnl`	Robust Sparse Hyperplane Classifiers: Application to Uncertain Molecular Profiling Data. J. Comput. Biol. Chiranjib Bhattacharyya, L. R. Grate, Michael I. Jordan, Laurent El Ghaoui, I. Saira Mian
2004	—	`conf`	Semi-supervised Learning via Gaussian Processes. NIPS Neil D. Lawrence, Michael I. Jordan
2004	—	`conf`	Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. NIPS Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei
2004	A*	`conf`	Variational methods for the Dirichlet process. ICML David M. Blei, Michael I. Jordan
2003	A	`conf`	A generalized mean field algorithm for variational inference in exponential families. UAI Eric P. Xing, Michael I. Jordan, Stuart Russell
2003	J	`jnl`	An Introduction to MCMC for Machine Learning. Mach. Learn. Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan
2003	—	`conf`	Autonomous Helicopter Flight via Reinforcement Learning. NIPS Andrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry
2003	J	`jnl`	Beyond Independent Components: Trees and Clusters. J. Mach. Learn. Res. Francis R. Bach, Michael I. Jordan
2003	A*	`conf`	Bug isolation via remote program sampling. PLDI Ben Liblit, Alex Aiken, Alice X. Zheng, Michael I. Jordan
2003	—	`conf`	Hierarchical Topic Models and the Nested Chinese Restaurant Process. NIPS David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum
2003	—	`conf`	Kalman filtering with intermittent observations. CDC Bruno Sinopoli, Luca Schenato, Massimo Franceschetti, Kameshwar Poolla, Michael I. Jordan, Shankar S. Sastry
2003	—	`conf`	Kernel Dimensionality Reduction for Supervised Learning. NIPS Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
2003	—	`conf`	Kernel independent component analysis. ICASSP (4) Francis R. Bach, Michael I. Jordan
2003	—	`conf`	LOGOS: a modular Bayesian model for de novo motif detection. CSB Eric P. Xing, Wei Wu, Michael I. Jordan, Richard M. Karp
2003	—	`conf`	Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. NIPS Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe
2003	J	`jnl`	Latent Dirichlet Allocation. J. Mach. Learn. Res. David M. Blei, Andrew Y. Ng, Michael I. Jordan
2003	—	`conf`	Learning Spectral Clustering. NIPS Francis R. Bach, Michael I. Jordan
2003	J	`jnl`	Matching Words and Pictures. J. Mach. Learn. Res. Kobus Barnard, Pinar Duygulu, David A. Forsyth, Nando de Freitas, David M. Blei, Michael I. Jordan
2003	A*	`conf`	Modeling annotated data. SIGIR David M. Blei, Michael I. Jordan
2003	—	`conf`	On the Concentration of Expectation and Approximate Inference in Layered Networks. NIPS XuanLong Nguyen, Michael I. Jordan
2003	—	`conf`	Semidefinite Relaxations for Approximate Inference on Graphs with Cycles. NIPS Martin J. Wainwright, Michael I. Jordan
2003	J	`jnl`	Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data. Signal Process. Chiranjib Bhattacharyya, L. R. Grate, Aylin Rizki, D. Radisky, F. J. Molina, Michael I. Jordan, Mina J. Bissell, I. Saira Mian
2003	—	`conf`	Statistical Debugging of Sampled Programs. NIPS Alice X. Zheng, Michael I. Jordan, Ben Liblit, Alex Aiken
2003	A*	`conf`	Support vector machines for analog circuit performance representation. DAC Fernando De Bernardinis, Michael I. Jordan, Alberto L. Sangiovanni-Vincentelli
2002	—	`conf`	A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences. NIPS Eric P. Xing, Michael I. Jordan, Richard M. Karp, Stuart Russell
2002	—	`conf`	A Minimal Intervention Principle for Coordinated Movement. NIPS Emanuel Todorov, Michael I. Jordan
2002	J	`jnl`	A Robust Minimax Approach to Classification. J. Mach. Learn. Res. Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan
2002	—	`conf`	Distance Metric Learning with Application to Clustering with Side-Information. NIPS Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart Russell
2002	J	`jnl`	Graphical Models: Foundations of Neural Computation. Pattern Anal. Appl. Michael I. Jordan, Terrence J. Sejnowski
2002	J	`jnl`	Kernel Independent Component Analysis. J. Mach. Learn. Res. Francis R. Bach, Michael I. Jordan
2002	—	`conf`	Learning Graphical Models with Mercer Kernels. NIPS Francis R. Bach, Michael I. Jordan
2002	A*	`conf`	Learning the Kernel Matrix with Semi-Definite Programming. ICML Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan
2002	A	`conf`	Loopy Belief Propogation and Gibbs Measures. UAI Sekhar Tatikonda, Michael I. Jordan
2002	—	`conf`	Robust Novelty Detection with Single-Class MPM. NIPS Gert R. G. Lanckriet, Laurent El Ghaoui, Michael I. Jordan
2002	C	`conf`	Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces. WABI L. R. Grate, Chiranjib Bhattacharyya, Michael I. Jordan, I. Saira Mian
2002	A	`conf`	Tree-dependent Component Analysis. UAI Francis R. Bach, Michael I. Jordan
2001	J	`jnl`	Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures. Neural Comput. Jinwen Ma, Lei Xu, Michael I. Jordan
2001	A*	`conf`	Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. ICML Andrew Y. Ng, Michael I. Jordan
2001	A	`conf`	Efficient Stepwise Selection in Decomposable Models. UAI Amol Deshpande, Minos N. Garofalakis, Michael I. Jordan
2001	A*	`conf`	Feature selection for high-dimensional genomic microarray data. ICML Eric P. Xing, Michael I. Jordan, Richard M. Karp
2001	—	`conf`	Latent Dirichlet Allocation. NIPS David M. Blei, Andrew Y. Ng, Michael I. Jordan
2001	A*	`conf`	Link Analysis, Eigenvectors and Stability. IJCAI Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan
2001	—	`conf`	Minimax Probability Machine. NIPS Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan
2001	—	`conf`	On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. NIPS Andrew Y. Ng, Michael I. Jordan
2001	—	`conf`	On Spectral Clustering: Analysis and an algorithm. NIPS Andrew Y. Ng, Michael I. Jordan, Yair Weiss
2001	A*	`conf`	Stable Algorithms for Link Analysis. SIGIR Alice X. Zheng, Andrew Y. Ng, Michael I. Jordan
2001	—	`conf`	Thin Junction Trees. NIPS Francis R. Bach, Michael I. Jordan
2000	J	`jnl`	Attractor Dynamics in Feedforward Neural Networks. Neural Comput. Lawrence K. Saul, Michael I. Jordan
2000	J	`jnl`	Bayesian parameter estimation via variational methods. Stat. Comput. Tommi S. Jaakkola, Michael I. Jordan
2000	J	`jnl`	Learning with Mixtures of Trees. J. Mach. Learn. Res. Marina Meila, Michael I. Jordan
2000	A	`conf`	PEGASUS: A policy search method for large MDPs and POMDPs. UAI Andrew Y. Ng, Michael I. Jordan
1999	J	`jnl`	An Introduction to Variational Methods for Graphical Models. Mach. Learn. Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul
1999	—	`conf`	Approximate Inference A lgorithms for Two-Layer Bayesian Networks. NIPS Andrew Y. Ng, Michael I. Jordan
1999	A	`conf`	Loopy Belief Propagation for Approximate Inference: An Empirical Study. UAI Kevin P. Murphy, Yair Weiss, Michael I. Jordan
1999	J	`jnl`	Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. Mach. Learn. Lawrence K. Saul, Michael I. Jordan
1999	J	`jnl`	Variational Probabilistic Inference and the QMR-DT Network. J. Artif. Intell. Res. Tommi S. Jaakkola, Michael I. Jordan
1998	—	`ch.`	A Mean Field Learning Algorithm for Unsupervised Neural Networks. Learning in Graphical Models Lawrence K. Saul, Michael I. Jordan
1998	—	`ed.`	Advances in Neural Information Processing Systems 10, [NIPS Conference, Denver, Colorado, USA, 1997] NIPS Michael I. Jordan, Michael J. Kearns, Sara A. Solla
1998	—	`ch.`	An Introduction to Variational Methods for Graphical Models. Learning in Graphical Models Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul
1998	—	`ch.`	Improving the Mean Field Approximation Via the Use of Mixture Distributions. Learning in Graphical Models Tommi S. Jaakkola, Michael I. Jordan
1998	—	`conf`	Learning from Dyadic Data. NIPS Thomas Hofmann, Jan Puzicha, Michael I. Jordan
1998	—	`book`	Learning in Graphical Models Michael I. Jordan
1998	A	`conf`	Mixture Representations for Inference and Learning in Boltzmann Machines. UAI Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan
1997	A	`conf`	A Variational Approach to Bayesian Logistic Regression Models and their Extensions. AISTATS Tommi S. Jaakkola, Michael I. Jordan
1997	—	`conf`	Adaptation in Speech Motor Control. NIPS John F. Houde, Michael I. Jordan
1997	—	`ed.`	Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2-5, 1996 NIPS Michael Mozer, Michael I. Jordan, Thomas Petsche
1997	A	`conf`	An Objective Function for Belief Net Triangulation. AISTATS Marina Meila, Michael I. Jordan
1997	—	`conf`	Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS Christopher M. Bishop, Neil D. Lawrence, Tommi S. Jaakkola, Michael I. Jordan
1997	—	`conf`	Estimating Dependency Structure as a Hidden Variable. NIPS Marina Meila, Michael I. Jordan
1997	J	`jnl`	Factorial Hidden Markov Models. Mach. Learn. Zoubin Ghahramani, Michael I. Jordan
1997	A	`conf`	Mixed Memory Markov Models. AISTATS Lawrence K. Saul, Michael I. Jordan
1997	—	`ch.`	Neural Networks. The Computer Science and Engineering Handbook Michael I. Jordan, Christopher M. Bishop
1997	J	`jnl`	Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Comput. Padhraic Smyth, David Heckerman, Michael I. Jordan
1996	—	`conf`	A Variational Principle for Model-based Morphing. NIPS Lawrence K. Saul, Michael I. Jordan
1996	J	`jnl`	Active Learning with Statistical Models CoRR David A. Cohn, Zoubin Ghahramani, Michael I. Jordan
1996	J	`jnl`	Active Learning with Statistical Models. J. Artif. Intell. Res. David A. Cohn, Zoubin Ghahramani, Michael I. Jordan
1996	A	`conf`	Computing upper and lower bounds on likelihoods in intractable networks. UAI Tommi S. Jaakkola, Michael I. Jordan
1996	—	`conf`	Hidden Markov Decision Trees. NIPS Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul
1996	J	`jnl`	Local linear perceptrons for classification. IEEE Trans. Neural Networks Ethem Alpaydin, Michael I. Jordan
1996	J	`jnl`	Mean Field Theory for Sigmoid Belief Networks CoRR Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan
1996	J	`jnl`	Mean Field Theory for Sigmoid Belief Networks. J. Artif. Intell. Res. Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan
1996	J	`jnl`	Neural Networks. ACM Comput. Surv. Michael I. Jordan, Christopher M. Bishop
1996	J	`jnl`	On Convergence Properties of the EM Algorithm for Gaussian Mixtures. Neural Comput. Lei Xu, Michael I. Jordan
1996	—	`conf`	Recursive Algorithms for Approximating Probabilities in Graphical Models. NIPS Tommi S. Jaakkola, Michael I. Jordan
1996	—	`conf`	Triangulation by Continuous Embedding. NIPS Marina Meila, Michael I. Jordan
1995	J	`jnl`	Convergence results for the EM approach to mixtures of experts architectures. Neural Networks Michael I. Jordan, Lei Xu
1995	—	`conf`	Exploiting Tractable Substructures in Intractable Networks. NIPS Lawrence K. Saul, Michael I. Jordan
1995	—	`conf`	Factorial Hidden Markov Models. NIPS Zoubin Ghahramani, Michael I. Jordan
1995	—	`conf`	Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS Tommi S. Jaakkola, Lawrence K. Saul, Michael I. Jordan
1995	—	`conf`	Learning Fine Motion by Markov Mixtures of Experts. NIPS Marina Meila, Michael I. Jordan
1995	—	`conf`	Reinforcement Learning by Probability Matching. NIPS Philip N. Sabes, Michael I. Jordan
1994	A*	`conf`	A Statistical Approach to Decision Tree Modeling. COLT Michael I. Jordan
1994	A*	`conf`	A Statistical Approach to Decision Tree Modeling. ICML Michael I. Jordan
1994	—	`conf`	Active Learning with Statistical Models. NIPS David A. Cohn, Zoubin Ghahramani, Michael I. Jordan
1994	—	`conf`	An Alternative Model for Mixtures of Experts. NIPS Lei Xu, Michael I. Jordan, Geoffrey E. Hinton
1994	—	`conf`	Boltzmann Chains and Hidden Markov Models. NIPS Lawrence K. Saul, Michael I. Jordan
1994	—	`conf`	Computational Structure of coordinate transformations: A generalization study. NIPS Zoubin Ghahramani, Daniel M. Wolpert, Michael I. Jordan
1994	—	`conf`	Forward dynamic models in human motor control: Psychophysical evidence. NIPS Daniel M. Wolpert, Zoubin Ghahramani, Michael I. Jordan
1994	J	`jnl`	Hierarchical Mixtures of Experts and the EM Algorithm. Neural Comput. Michael I. Jordan, Robert A. Jacobs
1994	A*	`conf`	Learning Without State-Estimation in Partially Observable Markovian Decision Processes. ICML Satinder P. Singh, Tommi S. Jaakkola, Michael I. Jordan
1994	J	`jnl`	Learning in Boltzmann Trees. Neural Comput. Lawrence K. Saul, Michael I. Jordan
1994	J	`jnl`	On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. Neural Comput. Tommi S. Jaakkola, Michael I. Jordan, Satinder P. Singh
1994	—	`conf`	Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. NIPS Tommi S. Jaakkola, Satinder Singh, Michael I. Jordan
1994	—	`conf`	Reinforcement Learning with Soft State Aggregation. NIPS Satinder Singh, Tommi S. Jaakkola, Michael I. Jordan
1993	—	`conf`	Convergence of Stochastic Iterative Dynamic Programming Algorithms. NIPS Tommi S. Jaakkola, Michael I. Jordan, Satinder Singh
1993	J	`jnl`	Learning piecewise control strategies in a modular neural network architecture. IEEE Trans. Syst. Man Cybern. Robert A. Jacobs, Michael I. Jordan
1993	A*	`conf`	Supervised Learning and Divide-and-Conquer: A Statistical Approach. ICML Michael I. Jordan, Robert A. Jacobs
1993	—	`conf`	Supervised learning from incomplete data via an EM approach. NIPS Zoubin Ghahramani, Michael I. Jordan
1993	—	`conf`	Task Decompostiion Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Machine Learning: From Theory to Applications Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto
1992	—	`conf`	A Dynamical Model of Priming and Repetition Blindness. NIPS Daphne Bavelier, Michael I. Jordan
1992	J	`jnl`	Forward Models: Supervised Learning with a Distal Teacher. Cogn. Sci. Michael I. Jordan, David E. Rumelhart
1991	J	`jnl`	Adaptive Mixtures of Local Experts. Neural Comput. Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton
1991	—	`conf`	Forward Dynamics Modeling of Speech Motor Control Using Physiological Data. NIPS Makoto Hirayama, Eric Vatikiotis-Bateson, Mitsuo Kawato, Michael I. Jordan
1991	—	`conf`	Hierarchies of Adaptive Experts. NIPS Michael I. Jordan, Robert A. Jacobs
1991	—	`conf`	Internal World Models and Supervised Learning. ML Michael I. Jordan, David E. Rumelhart
1991	J	`jnl`	Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Cogn. Sci. Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto
1990	B	`conf`	A IJCNN Pietro Mazzoni, Richard A. Andersen, Michael I. Jordan
1990	—	`conf`	A Competitive Modular Connectionist Architecture. NIPS Robert A. Jacobs, Michael I. Jordan
1989	—	`conf`	Learning to Control an Unstable System with Forward Modeling. NIPS Michael I. Jordan, Robert A. Jacobs