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ORCIDMichael I. Jordan
Person information
- affiliation: University of California, Berkeley, Department of Electrical Engineering and Computer Science
- affiliation: University of California, Berkeley, Department of Statistics
- affiliation: Massachusetts Institute of Technology, Center for Biological and Computational Learning
- award (2009): ACM - AAAI Allen Newell Award
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2020 – today
- 2025
[j129]Tianyi Lin
, Michael I. Jordan:
Perseus: a simple and optimal high-order method for variational inequalities. Math. Program. 209(1): 609-650 (2025)- [i356]Anastasios N. Angelopoulos, Michael I. Jordan, Ryan J. Tibshirani:
Gradient Equilibrium in Online Learning: Theory and Applications. CoRR abs/2501.08330 (2025) - [i355]Jivat Neet Kaur, Michael I. Jordan, Ahmed Alaa:
Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores. CoRR abs/2501.10139 (2025) - [i354]Michael Muehlebach, Zhiyu He, Michael I. Jordan:
The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective. CoRR abs/2501.15910 (2025) - [i353]Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus:
Prediction-Aware Learning in Multi-Agent Systems. CoRR abs/2501.19144 (2025) - [i352]Eugene Berta, David Holzmüller, Michael I. Jordan, Francis R. Bach:
Rethinking Early Stopping: Refine, Then Calibrate. CoRR abs/2501.19195 (2025) - [i351]Antoine Scheid, Etienne Boursier, Alain Durmus, Eric Moulines, Michael I. Jordan:
Learning Contracts in Hierarchical Multi-Agent Systems. CoRR abs/2501.19388 (2025) - [i350]Tianyu Guo, Hanlin Zhu, Ruiqi Zhang, Jiantao Jiao, Song Mei, Michael I. Jordan, Stuart Russell:
How Do LLMs Perform Two-Hop Reasoning in Context? CoRR abs/2502.13913 (2025) - 2024
- [j128]Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. SIAM J. Math. Data Sci. 6(2): 553-577 (2024) - [j127]Xiaowu Dai, Wenlu Xu, Yuan Qi, Michael I. Jordan:
Incentive-Aware Recommender Systems in Two-Sided Markets. Trans. Recomm. Syst. 2(4): 32:1-32:38 (2024) - [c416]Tianyi Lin, Marco Cuturi, Michael I. Jordan:
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. AISTATS 2024: 145-153 - [c415]Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab:
Delegating Data Collection in Decentralized Machine Learning. AISTATS 2024: 478-486 - [c414]Serena Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan:
On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry. AISTATS 2024: 1522-1530 - [c413]Eugene Berta, Francis R. Bach, Michael I. Jordan:
Classifier Calibration with ROC-Regularized Isotonic Regression. AISTATS 2024: 1972-1980 - [c412]Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan:
Privacy Can Arise Endogenously in an Economic System with Learning Agents. FORC 2024: 9:1-9:22 - [c411]Tatjana Chavdarova, Tong Yang, Matteo Pagliardini, Michael I. Jordan:
A Primal-Dual Approach to Solving Variational Inequalities with General Constraints. ICLR 2024 - [c410]Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan:
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. ICML 2024 - [c409]Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael I. Jordan, Joseph E. Gonzalez, Ion Stoica:
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. ICML 2024 - [c408]Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus:
Incentivized Learning in Principal-Agent Bandit Games. ICML 2024 - [c407]Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. ICML 2024 - [c406]Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik:
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions. ICRA 2024: 11668-11675 - [c405]Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar:
Data Acquisition via Experimental Design for Data Markets. NeurIPS 2024 - [c404]Alireza Fallah, Michael I. Jordan, Annie Ulichney:
Fair Allocation in Dynamic Mechanism Design. NeurIPS 2024 - [c403]Yi Zeng, Xuelin Yang, Li Chen, Cristian Canton Ferrer, Ming Jin, Michael I. Jordan, Ruoxi Jia:
Fairness-Aware Meta-Learning via Nash Bargaining. NeurIPS 2024 - [c402]Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus:
Unravelling in Collaborative Learning. NeurIPS 2024 - [c401]Yuval Dagan, Michael I. Jordan, Xuelin Yang, Lydia Zakynthinou, Nikita Zhivotovskiy:
Dimension-free Private Mean Estimation for Anisotropic Distributions. NeurIPS 2024 - [c400]Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus:
Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality. NeurIPS 2024 - [c399]Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart J. Russell:
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. NeurIPS 2024 - [c398]Alireza Fallah, Michael I. Jordan:
Contract Design With Safety Inspections. EC 2024: 616-638 - [i349]Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. CoRR abs/2401.16335 (2024) - [i348]Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian:
The Limits of Price Discrimination Under Privacy Constraints. CoRR abs/2402.08223 (2024) - [i347]Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian:
On Three-Layer Data Markets. CoRR abs/2402.09697 (2024) - [i346]Serena Wang, Michael I. Jordan, Katrina Ligett, R. Preston McAfee:
Information Elicitation in Agency Games. CoRR abs/2402.14005 (2024) - [i345]Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus:
Incentivized Learning in Principal-Agent Bandit Games. CoRR abs/2403.03811 (2024) - [i344]Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael I. Jordan, Joseph E. Gonzalez, Ion Stoica:
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. CoRR abs/2403.04132 (2024) - [i343]Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik, Michael I. Jordan:
AutoEval Done Right: Using Synthetic Data for Model Evaluation. CoRR abs/2403.07008 (2024) - [i342]Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar:
Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR abs/2403.13893 (2024) - [i341]Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan:
Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction. CoRR abs/2403.19605 (2024) - [i340]Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan:
Privacy Can Arise Endogenously in an Economic System with Learning Agents. CoRR abs/2404.10767 (2024) - [i339]Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan:
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. CoRR abs/2404.15746 (2024) - [i338]Ezinne Nwankwo, Michael I. Jordan, Angela Zhou:
Reduced-Rank Multi-objective Policy Learning and Optimization. CoRR abs/2404.18490 (2024) - [i337]Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart Russell:
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. CoRR abs/2405.04669 (2024) - [i336]Alireza Fallah, Michael I. Jordan, Annie Ulichney:
Fair Allocation in Dynamic Mechanism Design. CoRR abs/2406.00147 (2024) - [i335]Yi Zeng, Xuelin Yang, Li Chen, Cristian Canton Ferrer, Ming Jin, Michael I. Jordan, Ruoxi Jia:
Fairness-Aware Meta-Learning via Nash Bargaining. CoRR abs/2406.07029 (2024) - [i334]Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan:
Defection-Free Collaboration between Competitors in a Learning System. CoRR abs/2406.15898 (2024) - [i333]Vincent Blot, Anastasios N. Angelopoulos, Michael I. Jordan, Nicolas J.-B. Brunel:
Automatically Adaptive Conformal Risk Control. CoRR abs/2406.17819 (2024) - [i332]Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus:
Mitigating Externalities while Learning: an Online Version of the Coase Theorem. CoRR abs/2406.19824 (2024) - [i331]Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus:
Unravelling in Collaborative Learning. CoRR abs/2407.14332 (2024) - [i330]Tianyi Lin, Chi Jin, Michael I. Jordan:
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization. CoRR abs/2408.11974 (2024) - [i329]Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt:
Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry. CoRR abs/2409.03734 (2024) - [i328]Tianyu Guo, Druv Pai, Yu Bai, Jiantao Jiao, Michael I. Jordan, Song Mei:
Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs. CoRR abs/2410.13835 (2024) - [i327]Antoine Scheid, Etienne Boursier, Alain Durmus, Michael I. Jordan, Pierre Ménard, Eric Moulines, Michal Valko:
Optimal Design for Reward Modeling in RLHF. CoRR abs/2410.17055 (2024) - [i326]Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas A. Courtade, Alireza Fallah, Michael I. Jordan:
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy. CoRR abs/2410.18404 (2024) - [i325]Eric Zhao, Tatjana Chavdarova, Michael I. Jordan:
Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity. CoRR abs/2410.20649 (2024) - [i324]Yuval Dagan, Michael I. Jordan, Xuelin Yang, Lydia Zakynthinou, Nikita Zhivotovskiy:
Dimension-free Private Mean Estimation for Anisotropic Distributions. CoRR abs/2411.00775 (2024) - [i323]Jordan Lekeufack, Michael I. Jordan:
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints. CoRR abs/2412.08060 (2024) - 2023
- [j126]Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt:
Learning Equilibria in Matching Markets with Bandit Feedback. J. ACM 70(3): 19:1-19:46 (2023) - [j125]Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? J. Mach. Learn. Res. 24: 35:1-35:52 (2023) - [j124]Michael I. Jordan, Tianyi Lin, Manolis Zampetakis:
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. J. Mach. Learn. Res. 24: 38:1-38:46 (2023) - [j123]Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback. J. Mach. Learn. Res. 24: 53:1-53:45 (2023) - [j122]Bin Shi, Weijie Su, Michael I. Jordan:
On Learning Rates and Schrödinger Operators. J. Mach. Learn. Res. 24: 379:1-379:53 (2023) - [j121]Eric Xia, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan:
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning. J. Mach. Learn. Res. 24: 392:1-392:43 (2023) - [j120]Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Provably Efficient Reinforcement Learning with Linear Function Approximation. Math. Oper. Res. 48(3): 1496-1521 (2023) - [j119]Tianyi Lin, Michael I. Jordan:
Monotone Inclusions, Acceleration, and Closed-Loop Control. Math. Oper. Res. 48(4): 2353-2382 (2023) - [j118]Yuchen Zhang, Mingsheng Long, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael I. Jordan, Jianmin Wang:
Skilful nowcasting of extreme precipitation with NowcastNet. Nat. 619(7970): 526-532 (2023) - [j117]Mariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy:
Provably Personalized and Robust Federated Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c397]Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab:
Competition, Alignment, and Equilibria in Digital Marketplaces. AAAI 2023: 5689-5696 - [c396]Ruitu Xu, Yifei Min, Tianhao Wang, Michael I. Jordan, Zhaoran Wang, Zhuoran Yang:
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning. AISTATS 2023: 375-407 - [c395]Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan:
A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. AISTATS 2023: 2207-2261 - [c394]Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan:
Byzantine-Robust Federated Learning with Optimal Statistical Rates. AISTATS 2023: 3151-3178 - [c393]Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT 2023: 1166-1215 - [c392]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. COLT 2023: 4570-4597 - [c391]Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan:
Recommendation Systems with Distribution-Free Reliability Guarantees. COPA 2023: 175-193 - [c390]Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael I. Jordan, Zheng-Jun Zha:
Neural Dependencies Emerging from Learning Massive Categories. CVPR 2023: 11711-11720 - [c389]Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan:
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. ICLR 2023 - [c388]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean:
Modeling content creator incentives on algorithm-curated platforms. ICLR 2023 - [c387]Tong Yang, Michael I. Jordan, Tatjana Chavdarova:
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method. ICLR 2023 - [c386]Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan:
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. ICML 2023: 20351-20383 - [c385]Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar:
Federated Conformal Predictors for Distributed Uncertainty Quantification. ICML 2023: 22942-22964 - [c384]Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Online Learning in Stackelberg Games with an Omniscient Follower. ICML 2023: 42304-42316 - [c383]Banghua Zhu, Michael I. Jordan, Jiantao Jiao:
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. ICML 2023: 43037-43067 - [c382]Zhiwei (Tony) Qin, Rui Song, Jieping Ye, Hongtu Zhu, Michael I. Jordan:
KDD-2023 Workshop on Decision Intelligence and Analytics for Online Marketplaces. KDD 2023: 5878-5879 - [c381]Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song:
On Learning Necessary and Sufficient Causal Graphs. NeurIPS 2023 - [c380]Tiffany Ding, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani:
Class-Conditional Conformal Prediction with Many Classes. NeurIPS 2023 - [c379]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning. NeurIPS 2023 - [c378]Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab:
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. NeurIPS 2023 - [c377]Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S. Du:
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure. NeurIPS 2023 - [c376]Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark W. Barrett, Michael I. Jordan, Jiantao Jiao:
Towards Optimal Caching and Model Selection for Large Model Inference. NeurIPS 2023 - [c375]Banghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao:
Doubly-Robust Self-Training. NeurIPS 2023 - [c374]Romil Bhardwaj, Kirthevasan Kandasamy, Asim Biswal, Wenshuo Guo, Benjamin Hindman, Joseph Gonzalez, Michael I. Jordan, Ion Stoica:
Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback. OSDI 2023: 623-643 - [c373]Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan:
The Sample Complexity of Online Contract Design. EC 2023: 1188 - [c372]Chris Junchi Li, Michael I. Jordan:
Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. UAI 2023: 1230-1240 - [i322]Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic:
Prediction-Powered Inference. CoRR abs/2301.09633 (2023) - [i321]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. CoRR abs/2301.11270 (2023) - [i320]Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Online Learning in Stackelberg Games with an Omniscient Follower. CoRR abs/2301.11518 (2023) - [i319]Hengrui Cai, Yixin Wang, Michael I. Jordan, Rui Song:
On Learning Necessary and Sufficient Causal Graphs. CoRR abs/2301.12389 (2023) - [i318]Michael Muehlebach, Michael I. Jordan:
Accelerated First-Order Optimization under Nonlinear Constraints. CoRR abs/2302.00316 (2023) - [i317]Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. CoRR abs/2302.08300 (2023) - [i316]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning. CoRR abs/2302.10863 (2023) - [i315]Ruitu Xu, Yifei Min, Tianhao Wang, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning. CoRR abs/2303.04833 (2023) - [i314]Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan:
Online Learning in a Creator Economy. CoRR abs/2305.11381 (2023) - [i313]Serena Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan:
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry. CoRR abs/2305.14595 (2023) - [i312]Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar:
Federated Conformal Predictors for Distributed Uncertainty Quantification. CoRR abs/2305.17564 (2023) - [i311]Banghua Zhu, Mingyu Ding, Philip L. Jacobson, Ming Wu, Wei Zhan, Michael I. Jordan, Jiantao Jiao:
Doubly Robust Self-Training. CoRR abs/2306.00265 (2023) - [i310]Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark W. Barrett, Michael I. Jordan, Jiantao Jiao:
On Optimal Caching and Model Multiplexing for Large Model Inference. CoRR abs/2306.02003 (2023) - [i309]Banghua Zhu, Hiteshi Sharma, Felipe Vieira Frujeri, Shi Dong, Chenguang Zhu, Michael I. Jordan, Jiantao Jiao:
Fine-Tuning Language Models with Advantage-Induced Policy Alignment. CoRR abs/2306.02231 (2023) - [i308]Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan:
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. CoRR abs/2306.05592 (2023) - [i307]Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt:
Incentivizing High-Quality Content in Online Recommender Systems. CoRR abs/2306.07479 (2023) - [i306]Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi:
Provably Personalized and Robust Federated Learning. CoRR abs/2306.08393 (2023) - [i305]Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan, Ryan J. Tibshirani:
Class-Conditional Conformal Prediction With Many Classes. CoRR abs/2306.09335 (2023) - [i304]Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab:
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. CoRR abs/2306.14670 (2023) - [i303]Yang Cai, Michael I. Jordan, Tianyi Lin, Argyris Oikonomou, Emmanouil V. Vlatakis-Gkaragkounis:
Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds. CoRR abs/2306.16617 (2023) - [i302]Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan:
Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR abs/2307.00126 (2023) - [i301]Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff:
Incentive-Theoretic Bayesian Inference for Collaborative Science. CoRR abs/2307.03748 (2023) - [i300]Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
Scaff-PD: Communication Efficient Fair and Robust Federated Learning. CoRR abs/2307.13381 (2023) - [i299]Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab:
Delegating Data Collection in Decentralized Machine Learning. CoRR abs/2309.01837 (2023) - [i298]Neha S. Wadia, Yatin Dandi, Michael I. Jordan:
A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning. CoRR abs/2309.04877 (2023) - [i297]Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik:
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions. CoRR abs/2310.05921 (2023) - [i296]Michael I. Jordan, Tianyi Lin, Zhengyuan Zhou:
Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback. CoRR abs/2310.14085 (2023) - [i295]Tianyi Lin, Marco Cuturi, Michael I. Jordan:
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. CoRR abs/2310.14087 (2023) - [i294]Alireza Fallah, Michael I. Jordan:
Contract Design With Safety Inspections. CoRR abs/2311.02537 (2023) - [i293]Francisca Vasconcelos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos, Georgios Piliouras, Michael I. Jordan:
A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games. CoRR abs/2311.10859 (2023) - [i292]Eugene Berta, Francis R. Bach, Michael I. Jordan:
Classifier Calibration with ROC-Regularized Isotonic Regression. CoRR abs/2311.12436 (2023) - [i291]Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Michael I. Jordan:
Towards Optimal Statistical Watermarking. CoRR abs/2312.07930 (2023) - 2022
- [j116]Horia Mania, Michael I. Jordan, Benjamin Recht:
Active Learning for Nonlinear System Identification with Guarantees. J. Mach. Learn. Res. 23: 32:1-32:30 (2022) - [j115]Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan:
On the Complexity of Approximating Multimarginal Optimal Transport. J. Mach. Learn. Res. 23: 65:1-65:43 (2022) - [j114]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On the Efficiency of Entropic Regularized Algorithms for Optimal Transport. J. Mach. Learn. Res. 23: 137:1-137:42 (2022) - [j113]Kaichao You, Yong Liu, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long:
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs. J. Mach. Learn. Res. 23: 209:1-209:47 (2022) - [j112]Michael Muehlebach, Michael I. Jordan:
On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems. J. Mach. Learn. Res. 23: 256:1-256:47 (2022) - [j111]Nhat Ho, Chiao-Yu Yang, Michael I. Jordan:
Convergence Rates for Gaussian Mixtures of Experts. J. Mach. Learn. Res. 23: 323:1-323:81 (2022) - [j110]Adelson Chua, Michael I. Jordan, Rikky Muller:
SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier. IEEE J. Solid State Circuits 57(8): 2532-2544 (2022) - [j109]Bin Shi, Simon S. Du, Michael I. Jordan, Weijie J. Su:
Understanding the acceleration phenomenon via high-resolution differential equations. Math. Program. 195(1): 79-148 (2022) - [j108]Tianyi Lin, Michael I. Jordan:
A control-theoretic perspective on optimal high-order optimization. Math. Program. 195(1): 929-975 (2022) - [j107]Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan:
KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail, and Beyond. SIGKDD Explor. 24(2): 78-80 (2022) - [j106]Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan:
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. SIAM J. Math. Data Sci. 4(2): 634-648 (2022) - [j105]Wenshuo Guo, Serena Lutong Wang, Peng Ding, Yixin Wang, Michael I. Jordan:
Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias. Trans. Mach. Learn. Res. 2022 (2022) - [c371]Nhat Ho, Tianyi Lin, Michael I. Jordan:
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. AISTATS 2022: 896-921 - [c370]Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS 2022: 1219-1250 - [c369]Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica:
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. AISTATS 2022: 6200-6224 - [c368]Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan:
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. AISTATS 2022: 9793-9826 - [c367]Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan:
Partial Identification with Noisy Covariates: A Robust Optimization Approach. CLeaR 2022: 318-335 - [c366]Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan:
Optimal Mean Estimation without a Variance. COLT 2022: 356-357 - [c365]Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan:
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. COLT 2022: 909-981 - [c364]Anastasios N. Angelopoulos, Amit Pal Singh Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano:
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. ICML 2022: 717-730 - [c363]Wenshuo Guo, Michael I. Jordan, Ellen Vitercik:
No-Regret Learning in Partially-Informed Auctions. ICML 2022: 8039-8055 - [c362]Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. ICML 2022: 13441-13467 - [c361]Zhihan Liu, Miao Lu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy. ICML 2022: 13870-13911 - [c360]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Robust Estimation for Non-parametric Families via Generative Adversarial Networks. ISIT 2022: 1100-1105 - [c359]Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan:
Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail and Beyond. KDD 2022: 4898-4899 - [c358]Jian Zhang, Jian Tang, Yiran Chen, Jie Liu, Jieping Ye, Marilyn Wolf, Vijaykrishnan Narayanan, Mani B. Srivastava, Michael I. Jordan, Victor Bahl:
The 5th Artificial Intelligence of Things (AIoT) Workshop. KDD 2022: 4912-4913 - [c357]Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael I. Jordan, Zheng-Jun Zha:
Rank Diminishing in Deep Neural Networks. NeurIPS 2022 - [c356]Wenshuo Guo, Michael I. Jordan, Angela Zhou:
Off-Policy Evaluation with Policy-Dependent Optimization Response. NeurIPS 2022 - [c355]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
On-Demand Sampling: Learning Optimally from Multiple Distributions. NeurIPS 2022 - [c354]Michael I. Jordan, Tianyi Lin, Emmanouil V. Vlatakis-Gkaragkounis:
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. NeurIPS 2022 - [c353]Michael I. Jordan, Yixin Wang, Angela Zhou:
Empirical Gateaux Derivatives for Causal Inference. NeurIPS 2022 - [c352]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS 2022 - [c351]Tianyi Lin, Zeyu Zheng, Michael I. Jordan:
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. NeurIPS 2022 - [c350]Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. NeurIPS 2022 - [c349]Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. NeurIPS 2022 - [c348]Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan:
Robust Calibration with Multi-domain Temperature Scaling. NeurIPS 2022 - [c347]Elior Rahmani, Michael I. Jordan, Nir Yosef:
Identifying Systematic Variation at the Single-Cell Level by Leveraging Low-Resolution Population-Level Data. RECOMB 2022: 371 - [c346]Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu:
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. EC 2022: 471-472 - [i290]Wenlong Mou, Koulik Khamaru, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
Optimal variance-reduced stochastic approximation in Banach spaces. CoRR abs/2201.08518 (2022) - [i289]Koulik Khamaru, Eric Xia, Martin J. Wainwright, Michael I. Jordan:
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning. CoRR abs/2201.08536 (2022) - [i288]Mariel A. Werner, Anastasios Angelopoulos, Stephen Bates, Michael I. Jordan:
Online Active Learning with Dynamic Marginal Gain Thresholding. CoRR abs/2201.10547 (2022) - [i287]Elynn Y. Chen, Rui Song, Michael I. Jordan:
Reinforcement Learning with Heterogeneous Data: Estimation and Inference. CoRR abs/2202.00088 (2022) - [i286]Banghua Zhu, Jiantao Jiao, Michael I. Jordan:
Robust Estimation for Nonparametric Families via Generative Adversarial Networks. CoRR abs/2202.01269 (2022) - [i285]Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, Michael I. Jordan:
Conformal prediction for the design problem. CoRR abs/2202.03613 (2022) - [i284]Elynn Y. Chen, Michael I. Jordan, Sai Li:
Transferred Q-learning. CoRR abs/2202.04709 (2022) - [i283]Anastasios N. Angelopoulos, Amit P. S. Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano:
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. CoRR abs/2202.05265 (2022) - [i282]Matteo Pagliardini, Gilberto Manunza, Martin Jaggi, Michael I. Jordan, Tatjana Chavdarova:
Improving Generalization via Uncertainty Driven Perturbations. CoRR abs/2202.05737 (2022) - [i281]Wenshuo Guo, Michael I. Jordan, Ellen Vitercik:
No-Regret Learning in Partially-Informed Auctions. CoRR abs/2202.10606 (2022) - [i280]Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan:
Partial Identification with Noisy Covariates: A Robust Optimization Approach. CoRR abs/2202.10665 (2022) - [i279]Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu:
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. CoRR abs/2202.10678 (2022) - [i278]Boxiang Lyu, Qinglin Meng, Shuang Qiu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan:
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach. CoRR abs/2202.12797 (2022) - [i277]Wenshuo Guo, Michael I. Jordan, Angela Zhou:
Off-Policy Evaluation with Policy-Dependent Optimization Response. CoRR abs/2202.12958 (2022) - [i276]Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. CoRR abs/2203.03684 (2022) - [i275]Alessandro Barp, Lancelot Da Costa, Guilherme França, Karl J. Friston, Mark A. Girolami, Michael I. Jordan, Grigorios A. Pavliotis:
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents. CoRR abs/2203.10592 (2022) - [i274]Michael I. Jordan, Tianyi Lin, Manolis Zampetakis:
First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. CoRR abs/2204.03132 (2022) - [i273]Tianyi Lin, Michael I. Jordan:
Perseus: A Simple High-Order Regularization Method for Variational Inequalities. CoRR abs/2205.03202 (2022) - [i272]Stephen Bates, Michael I. Jordan, Michael Sklar, Jake A. Soloff:
Principal-Agent Hypothesis Testing. CoRR abs/2205.06812 (2022) - [i271]Sarah E. Chasins, Alvin Cheung, Natacha Crooks, Ali Ghodsi, Ken Goldberg, Joseph E. Gonzalez, Joseph M. Hellerstein, Michael I. Jordan, Anthony D. Joseph, Michael W. Mahoney, Aditya G. Parameswaran, David A. Patterson, Raluca Ada Popa, Koushik Sen, Scott Shenker, Dawn Song, Ion Stoica:
The Sky Above The Clouds. CoRR abs/2205.07147 (2022) - [i270]Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. CoRR abs/2205.07217 (2022) - [i269]Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan:
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees. CoRR abs/2205.11765 (2022) - [i268]Michael I. Jordan, Tianyi Lin, Emmanouil V. Vlatakis-Gkaragkounis:
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. CoRR abs/2206.02041 (2022) - [i267]Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan:
Robust Calibration with Multi-domain Temperature Scaling. CoRR abs/2206.02757 (2022) - [i266]Tianyi Lin, Michael I. Jordan:
A Continuous-Time Perspective on Monotone Equation Problems. CoRR abs/2206.04770 (2022) - [i265]Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael I. Jordan, Zheng-Jun Zha:
Rank Diminishing in Deep Neural Networks. CoRR abs/2206.06072 (2022) - [i264]Simon S. Du, Gauthier Gidel, Michael I. Jordan, Chris Junchi Li:
Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. CoRR abs/2206.08573 (2022) - [i263]Tong Yang, Michael I. Jordan, Tatjana Chavdarova:
Solving Constrained Variational Inequalities via an Interior Point Method. CoRR abs/2206.10575 (2022) - [i262]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean:
Modeling Content Creator Incentives on Algorithm-Curated Platforms. CoRR abs/2206.13102 (2022) - [i261]Melih Elibol, Vinamra Benara, Samyu Yagati, Lianmin Zheng, Alvin Cheung, Michael I. Jordan, Ion Stoica:
NumS: Scalable Array Programming for the Cloud. CoRR abs/2206.14276 (2022) - [i260]Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan:
Recommendation Systems with Distribution-Free Reliability Guarantees. CoRR abs/2207.01609 (2022) - [i259]Karl Krauth, Yixin Wang, Michael I. Jordan:
Breaking Feedback Loops in Recommender Systems with Causal Inference. CoRR abs/2207.01616 (2022) - [i258]Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan:
Mechanisms that Incentivize Data Sharing in Federated Learning. CoRR abs/2207.04557 (2022) - [i257]Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. CoRR abs/2207.06343 (2022) - [i256]Tatjana Chavdarova, Ya-Ping Hsieh, Michael I. Jordan:
Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata. CoRR abs/2207.07105 (2022) - [i255]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR abs/2208.05363 (2022) - [i254]Paula Gradu, Tijana Zrnic, Yixin Wang, Michael I. Jordan:
Valid Inference after Causal Discovery. CoRR abs/2208.05949 (2022) - [i253]Michael I. Jordan, Yixin Wang, Angela Zhou:
Empirical Gateaux Derivatives for Causal Inference. CoRR abs/2208.13701 (2022) - [i252]Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab:
Competition, Alignment, and Equilibria in Digital Marketplaces. CoRR abs/2208.14423 (2022) - [i251]Tianyi Lin, Zeyu Zheng, Michael I. Jordan:
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. CoRR abs/2209.05045 (2022) - [i250]Michael I. Jordan, Tianyi Lin, Manolis Zampetakis:
On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization. CoRR abs/2209.12463 (2022) - [i249]Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan:
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. CoRR abs/2209.15634 (2022) - [i248]Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan:
QuTE: decentralized multiple testing on sensor networks with false discovery rate control. CoRR abs/2210.04334 (2022) - [i247]Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan:
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design. CoRR abs/2210.10278 (2022) - [i246]Nika Haghtalab, Michael I. Jordan, Eric Zhao:
On-Demand Sampling: Learning Optimally from Multiple Distributions. CoRR abs/2210.12529 (2022) - [i245]Tianyi Lin, Panayotis Mertikopoulos, Michael I. Jordan:
Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee. CoRR abs/2210.12860 (2022) - [i244]Tatjana Chavdarova, Matteo Pagliardini, Tong Yang, Michael I. Jordan:
Revisiting the ACVI Method for Constrained Variational Inequalities. CoRR abs/2210.15659 (2022) - [i243]Chris Junchi Li, Angela Yuan, Gauthier Gidel, Michael I. Jordan:
Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization. CoRR abs/2210.17550 (2022) - [i242]Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan:
The Sample Complexity of Online Contract Design. CoRR abs/2211.05732 (2022) - [i241]Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael I. Jordan, Zheng-Jun Zha:
Neural Dependencies Emerging from Learning Massive Categories. CoRR abs/2211.12339 (2022) - [i240]Xiaowu Dai, Yuan Qi, Michael I. Jordan:
Incentive-Aware Recommender Systems in Two-Sided Markets. CoRR abs/2211.15381 (2022) - 2021
- [j104]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM 68(2): 11:1-11:29 (2021) - [j103]Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, Michael I. Jordan:
Distribution-free, Risk-controlling Prediction Sets. J. ACM 68(6): 43:1-43:34 (2021) - [j102]Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan:
Asynchronous Online Testing of Multiple Hypotheses. J. Mach. Learn. Res. 22: 33:1-33:39 (2021) - [j101]Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. J. Mach. Learn. Res. 22: 42:1-42:41 (2021) - [j100]Michael Muehlebach, Michael I. Jordan:
Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives. J. Mach. Learn. Res. 22: 73:1-73:50 (2021) - [j99]Ashia C. Wilson, Ben Recht, Michael I. Jordan:
A Lyapunov Analysis of Accelerated Methods in Optimization. J. Mach. Learn. Res. 22: 113:1-113:34 (2021) - [j98]Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan:
Bandit Learning in Decentralized Matching Markets. J. Mach. Learn. Res. 22: 211:1-211:34 (2021) - [j97]Xiaowu Dai, Michael I. Jordan:
Learning Strategies in Decentralized Matching Markets under Uncertain Preferences. J. Mach. Learn. Res. 22: 260:1-260:50 (2021) - [j96]Jelena Diakonikolas, Michael I. Jordan:
Generalized Momentum-Based Methods: A Hamiltonian Perspective. SIAM J. Optim. 31(1): 915-944 (2021) - [j95]Koulik Khamaru, Ashwin Pananjady, Feng Ruan, Martin J. Wainwright, Michael I. Jordan:
Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis. SIAM J. Math. Data Sci. 3(4): 1013-1040 (2021) - [c345]Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan:
Learning from eXtreme Bandit Feedback. AAAI 2021: 8732-8740 - [c344]Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan:
Robustness Guarantees for Mode Estimation with an Application to Bandits. AAAI 2021: 9277-9284 - [c343]Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan:
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. AISTATS 2021: 262-270 - [c342]Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan:
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. AISTATS 2021: 2746-2754 - [c341]Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry:
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CDC 2021: 742-749 - [c340]Wenshuo Guo, Michael I. Jordan, Tianyi Lin:
A Variational Inequality Approach to Bayesian Regression Games. CDC 2021: 795-802 - [c339]Lisa Dunlap, Kirthevasan Kandasamy, Ujval Misra, Richard Liaw, Michael I. Jordan, Ion Stoica, Joseph E. Gonzalez:
Elastic Hyperparameter Tuning on the Cloud. SoCC 2021: 33-46 - [c338]Chris Junchi Li, Michael I. Jordan:
Stochastic Approximation for Online Tensorial Independent Component Analysis. COLT 2021: 3051-3106 - [c337]Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg:
The Stereotyping Problem in Collaboratively Filtered Recommender Systems. EAAMO 2021: 6:1-6:10 - [c336]Anastasios Nikolas Angelopoulos, Stephen Bates, Michael I. Jordan, Jitendra Malik:
Uncertainty Sets for Image Classifiers using Conformal Prediction. ICLR 2021 - [c335]Esther Rolf, Theodora T. Worledge, Benjamin Recht, Michael I. Jordan:
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. ICML 2021: 9040-9051 - [c334]Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph Gonzalez:
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism. ICML 2021: 10236-10246 - [c333]Nilesh Tripuraneni, Chi Jin, Michael I. Jordan:
Provable Meta-Learning of Linear Representations. ICML 2021: 10434-10443 - [c332]Jian Zhang, Jian Tang, Yiran Chen, Jie Liu, Jieping Ye, Marilyn Wolf, Vijaykrishnan Narayanan, Mani Srivastava, Michael I. Jordan, Victor Bahl:
The 4th Artificial Intelligence of Things (AIoT) Workshop. KDD 2021: 4179-4180 - [c331]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus:
On Component Interactions in Two-Stage Recommender Systems. NeurIPS 2021: 2744-2757 - [c330]Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt:
Learning Equilibria in Matching Markets from Bandit Feedback. NeurIPS 2021: 3323-3335 - [c329]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. NeurIPS 2021: 3401-3412 - [c328]Xiaowu Dai, Michael I. Jordan:
Learning in Multi-Stage Decentralized Matching Markets. NeurIPS 2021: 12798-12809 - [c327]Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan:
Tactical Optimism and Pessimism for Deep Reinforcement Learning. NeurIPS 2021: 12849-12863 - [c326]Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan:
Test-time Collective Prediction. NeurIPS 2021: 13719-13731 - [c325]Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan:
Who Leads and Who Follows in Strategic Classification? NeurIPS 2021: 15257-15269 - [c324]Yufeng Zhang, Siyu Chen, Zhuoran Yang, Michael I. Jordan, Zhaoran Wang:
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic. NeurIPS 2021: 15993-16006 - [c323]Wenshuo Guo, Michael I. Jordan, Emmanouil Zampetakis:
Robust Learning of Optimal Auctions. NeurIPS 2021: 21273-21284 - [c322]Ghassen Jerfel, Serena Lutong Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan:
Variational refinement for importance sampling using the forward Kullback-Leibler divergence. UAI 2021: 1819-1829 - [c321]Adelson Chua, Michael I. Jordan, Rikky Muller:
A 1.5nJ/cls Unsupervised Online Learning Classifier for Seizure Detection. VLSI Circuits 2021: 1-2 - [i239]Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, Michael I. Jordan:
Distribution-Free, Risk-Controlling Prediction Sets. CoRR abs/2101.02703 (2021) - [i238]Anastasios N. Angelopoulos, Stephen Bates, Tijana Zrnic, Michael I. Jordan:
Private Prediction Sets. CoRR abs/2102.06202 (2021) - [i237]Xiaowu Dai, Michael I. Jordan:
Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors. CoRR abs/2102.06988 (2021) - [i236]Ani Adhikari, John DeNero, Michael I. Jordan:
Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley. CoRR abs/2102.09391 (2021) - [i235]Esther Rolf, Theodora Worledge, Benjamin Recht, Michael I. Jordan:
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. CoRR abs/2103.03399 (2021) - [i234]Wenshuo Guo, Michael I. Jordan, Tianyi Lin:
A Variational Inequality Approach to Bayesian Regression Games. CoRR abs/2103.13509 (2021) - [i233]Tyler Westenbroek, Max Simchowitz, Michael I. Jordan, S. Shankar Sastry:
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. CoRR abs/2103.15010 (2021) - [i232]Wenshuo Guo, Serena Lutong Wang, Peng Ding, Yixin Wang, Michael I. Jordan:
Multi-Source Causal Inference Using Control Variates. CoRR abs/2103.16689 (2021) - [i231]Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR abs/2104.13326 (2021) - [i230]Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan:
Parallelizing Contextual Linear Bandits. CoRR abs/2105.10590 (2021) - [i229]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. CoRR abs/2105.14363 (2021) - [i228]Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez:
PAC Best Arm Identification Under a Deadline. CoRR abs/2106.03221 (2021) - [i227]Wenshuo Guo, Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
Online Learning of Competitive Equilibria in Exchange Economies. CoRR abs/2106.06616 (2021) - [i226]Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan:
Test-time Collective Prediction. CoRR abs/2106.12012 (2021) - [i225]Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan:
Who Leads and Who Follows in Strategic Classification? CoRR abs/2106.12529 (2021) - [i224]Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg:
The Stereotyping Problem in Collaboratively Filtered Recommender Systems. CoRR abs/2106.12622 (2021) - [i223]Koulik Khamaru, Eric Xia, Martin J. Wainwright, Michael I. Jordan:
Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning. CoRR abs/2106.14352 (2021) - [i222]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus:
On component interactions in two-stage recommender systems. CoRR abs/2106.14979 (2021) - [i221]Ghassen Jerfel, Serena Lutong Wang, Clara Fannjiang, Katherine A. Heller, Yi-An Ma, Michael I. Jordan:
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence. CoRR abs/2106.15980 (2021) - [i220]Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan:
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. CoRR abs/2107.00464 (2021) - [i219]Wenshuo Guo, Michael I. Jordan, Manolis Zampetakis:
Robust Learning of Optimal Auctions. CoRR abs/2107.06259 (2021) - [i218]Michael Muehlebach, Michael I. Jordan:
On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems. CoRR abs/2107.08225 (2021) - [i217]Mohammad Rasouli, Michael I. Jordan:
Data Sharing Markets. CoRR abs/2107.08630 (2021) - [i216]Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt:
Learning Equilibria in Matching Markets from Bandit Feedback. CoRR abs/2108.08843 (2021) - [i215]Yixin Wang, Michael I. Jordan:
Desiderata for Representation Learning: A Causal Perspective. CoRR abs/2109.03795 (2021) - [i214]Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès, Michael I. Jordan, Lihua Lei:
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control. CoRR abs/2110.01052 (2021) - [i213]Adelson Chua, Michael I. Jordan, Rikky Muller:
SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier. CoRR abs/2110.02169 (2021) - [i212]Michael I. Jordan, Keli Liu, Feng Ruan:
On the Self-Penalization Phenomenon in Feature Selection. CoRR abs/2110.05852 (2021) - [i211]Kaichao You, Yong Liu, Jianmin Wang, Michael I. Jordan, Mingsheng Long:
Ranking and Tuning Pre-trained Models: A New Paradigm of Exploiting Model Hubs. CoRR abs/2110.10545 (2021) - [i210]Reese Pathak, Rajat Sen, Nikhil Rao, N. Benjamin Erichson, Michael I. Jordan, Inderjit S. Dhillon:
Cluster-and-Conquer: A Framework For Time-Series Forecasting. CoRR abs/2110.14011 (2021) - [i209]Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. CoRR abs/2111.04873 (2021) - [i208]Tianyi Lin, Michael I. Jordan:
On Monotone Inclusions, Acceleration and Closed-Loop Control. CoRR abs/2111.08093 (2021) - [i207]Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, Michael I. Jordan:
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning. CoRR abs/2112.05923 (2021) - [i206]Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers? CoRR abs/2112.13521 (2021) - [i205]Yufeng Zhang, Siyu Chen, Zhuoran Yang, Michael I. Jordan, Zhaoran Wang:
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic. CoRR abs/2112.13530 (2021) - [i204]Tatjana Chavdarova, Michael I. Jordan, Manolis Zampetakis:
Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations. CoRR abs/2112.13826 (2021) - [i203]Xiang Li, Wenhao Yang, Zhihua Zhang, Michael I. Jordan:
Polyak-Ruppert Averaged Q-Leaning is Statistically Efficient. CoRR abs/2112.14582 (2021) - [i202]Chris Junchi Li, Michael I. Jordan:
Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems. CoRR abs/2112.14738 (2021) - 2020
- [j94]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. J. Mach. Learn. Res. 21: 43:1-43:36 (2020) - [j93]Lihua Lei, Michael I. Jordan:
On the Adaptivity of Stochastic Gradient-Based Optimization. SIAM J. Optim. 30(2): 1473-1500 (2020) - [c320]Jianbo Chen, Michael I. Jordan:
LS-Tree: Model Interpretation When the Data Are Linguistic. AAAI 2020: 3454-3461 - [c319]Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song:
Cost-Effective Incentive Allocation via Structured Counterfactual Inference. AAAI 2020: 4997-5004 - [c318]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
ML-LOO: Detecting Adversarial Examples with Feature Attribution. AAAI 2020: 6639-6647 - [c317]Lydia T. Liu, Horia Mania, Michael I. Jordan:
Competing Bandits in Matching Markets. AISTATS 2020: 1618-1628 - [c316]Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett:
Langevin Monte Carlo without smoothness. AISTATS 2020: 1716-1726 - [c315]Esther Rolf, Michael I. Jordan, Benjamin Recht:
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. AISTATS 2020: 1759-1769 - [c314]Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models. AISTATS 2020: 1866-1876 - [c313]Wenshuo Guo, Nhat Ho, Michael I. Jordan:
Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter. AISTATS 2020: 2088-2097 - [c312]Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan:
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. AISTATS 2020: 3003-3014 - [c311]Tijana Zrnic, Daniel L. Jiang, Aaditya Ramdas, Michael I. Jordan:
The Power of Batching in Multiple Hypothesis Testing. AISTATS 2020: 3806-3815 - [c310]Tianyi Lin, Chengyou Fan, Mengdi Wang, Michael I. Jordan:
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient. ACC 2020: 126-131 - [c309]Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry:
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games. AAMAS 2020: 860-868 - [c308]Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry:
High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. CDC 2020: 4275-4280 - [c307]Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Provably efficient reinforcement learning with linear function approximation. COLT 2020: 2137-2143 - [c306]Tianyi Lin, Chi Jin, Michael I. Jordan:
Near-Optimal Algorithms for Minimax Optimization. COLT 2020: 2738-2779 - [c305]Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. COLT 2020: 2947-2997 - [c304]Adelson Chua, Michael I. Jordan, Rikky Muller:
Unsupervised Online Learning for Long-Term High Sensitivity Seizure Detection. EMBC 2020: 528-531 - [c303]Melih Elibol, Lihua Lei, Michael I. Jordan:
Variance Reduction With Sparse Gradients. ICLR 2020 - [c302]Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan:
Stochastic Gradient and Langevin Processes. ICML 2020: 1810-1819 - [c301]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML 2020: 4880-4889 - [c300]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML 2020: 5736-5746 - [c299]Tianyi Lin, Chi Jin, Michael I. Jordan:
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. ICML 2020: 6083-6093 - [c298]Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan:
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. ICML 2020: 6161-6171 - [c297]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett:
On Approximate Thompson Sampling with Langevin Algorithms. ICML 2020: 6797-6807 - [c296]Michael Muehlebach, Michael I. Jordan:
Continuous-time Lower Bounds for Gradient-based Algorithms. ICML 2020: 7088-7096 - [c295]Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan:
Learning to Score Behaviors for Guided Policy Optimization. ICML 2020: 7445-7454 - [c294]Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan:
Projection Robust Wasserstein Distance and Riemannian Optimization. NeurIPS 2020 - [c293]Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan:
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. NeurIPS 2020 - [c292]Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier:
Decision-Making with Auto-Encoding Variational Bayes. NeurIPS 2020 - [c291]Nilesh Tripuraneni, Michael I. Jordan, Chi Jin:
On the Theory of Transfer Learning: The Importance of Task Diversity. NeurIPS 2020 - [c290]Serena Lutong Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan:
Robust Optimization for Fairness with Noisy Protected Groups. NeurIPS 2020 - [c289]Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Calibration with Lower Bias and Variance in Domain Adaptation. NeurIPS 2020 - [c288]Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan:
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. NeurIPS 2020 - [c287]Jianbo Chen, Michael I. Jordan, Martin J. Wainwright:
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack. SP 2020: 1277-1294 - [i201]Melih Elibol, Lihua Lei, Michael I. Jordan:
Variance Reduction with Sparse Gradients. CoRR abs/2001.09623 (2020) - [i200]Tianyi Lin, Chi Jin, Michael I. Jordan:
Near-Optimal Algorithms for Minimax Optimization. CoRR abs/2002.02417 (2020) - [i199]Michael Muehlebach, Michael I. Jordan:
Continuous-time Lower Bounds for Gradient-based Algorithms. CoRR abs/2002.03546 (2020) - [i198]Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan:
Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms. CoRR abs/2002.04783 (2020) - [i197]Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan:
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. CoRR abs/2002.05359 (2020) - [i196]Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier:
Decision-Making with Auto-Encoding Variational Bayes. CoRR abs/2002.07217 (2020) - [i195]Serena Lutong Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan:
Robust Optimization for Fairness with Noisy Protected Groups. CoRR abs/2002.09343 (2020) - [i194]Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan:
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. CoRR abs/2002.09806 (2020) - [i193]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On Thompson Sampling with Langevin Algorithms. CoRR abs/2002.10002 (2020) - [i192]Nilesh Tripuraneni, Chi Jin, Michael I. Jordan:
Provable Meta-Learning of Linear Representations. CoRR abs/2002.11684 (2020) - [i191]Michael Muehlebach, Michael I. Jordan:
Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives. CoRR abs/2002.12493 (2020) - [i190]Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan:
Robustness Guarantees for Mode Estimation with an Application to Bandits. CoRR abs/2003.02932 (2020) - [i189]Esther Rolf, Michael I. Jordan, Benjamin Recht:
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. CoRR abs/2003.05955 (2020) - [i188]Koulik Khamaru, Ashwin Pananjady, Feng Ruan, Martin J. Wainwright, Michael I. Jordan:
Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis. CoRR abs/2003.07337 (2020) - [i187]Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. CoRR abs/2004.04719 (2020) - [i186]Bin Shi, Weijie J. Su, Michael I. Jordan:
On Learning Rates and Schrödinger Operators. CoRR abs/2004.06977 (2020) - [i185]Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
Mechanism Design with Bandit Feedback. CoRR abs/2004.08924 (2020) - [i184]Max Rabinovich, Michael I. Jordan, Martin J. Wainwright:
Lower bounds in multiple testing: A framework based on derandomized proxies. CoRR abs/2005.03725 (2020) - [i183]Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Instability, Computational Efficiency and Statistical Accuracy. CoRR abs/2005.11411 (2020) - [i182]Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan:
Projection Robust Wasserstein Distance and Riemannian Optimization. CoRR abs/2006.07458 (2020) - [i181]Horia Mania, Michael I. Jordan, Benjamin Recht:
Active Learning for Nonlinear System Identification with Guarantees. CoRR abs/2006.10277 (2020) - [i180]Nilesh Tripuraneni, Michael I. Jordan, Chi Jin:
On the Theory of Transfer Learning: The Importance of Task Diversity. CoRR abs/2006.11650 (2020) - [i179]Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan:
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. CoRR abs/2006.12301 (2020) - [i178]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. CoRR abs/2007.00699 (2020) - [i177]Daniel Ting, Michael I. Jordan:
Manifold Learning via Manifold Deflation. CoRR abs/2007.03315 (2020) - [i176]Wenshuo Guo, Mihaela Curmei, Serena Lutong Wang, Benjamin Recht, Michael I. Jordan:
Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty. CoRR abs/2007.05647 (2020) - [i175]Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett:
Optimal Robust Linear Regression in Nearly Linear Time. CoRR abs/2007.08137 (2020) - [i174]Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Calibration with Lower Bias and Variance in Domain Adaptation. CoRR abs/2007.08259 (2020) - [i173]Yuchen Zhang, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
On Localized Discrepancy for Domain Adaptation. CoRR abs/2008.06242 (2020) - [i172]Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan:
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. CoRR abs/2008.12690 (2020) - [i171]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus:
Exploration in two-stage recommender systems. CoRR abs/2009.08956 (2020) - [i170]Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan:
Learning from eXtreme Bandit Feedback. CoRR abs/2009.12947 (2020) - [i169]Anastasios Angelopoulos, Stephen Bates, Jitendra Malik, Michael I. Jordan:
Uncertainty Sets for Image Classifiers using Conformal Prediction. CoRR abs/2009.14193 (2020) - [i168]Xiaowu Dai, Michael I. Jordan:
Learning Strategies in Decentralized Matching Markets under Uncertain Preferences. CoRR abs/2011.00159 (2020) - [i167]Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez:
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism. CoRR abs/2011.00330 (2020) - [i166]Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan:
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. CoRR abs/2011.00364 (2020) - [i165]Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan:
Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations. CoRR abs/2011.04622 (2020) - [i164]Karl Krauth, Sarah Dean, Alex Zhao, Wenshuo Guo, Mihaela Curmei, Benjamin Recht, Michael I. Jordan:
Do Offline Metrics Predict Online Performance in Recommender Systems? CoRR abs/2011.07931 (2020) - [i163]Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan:
Optimal Mean Estimation without a Variance. CoRR abs/2011.12433 (2020) - [i162]Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan:
Bandit Learning in Decentralized Matching Markets. CoRR abs/2012.07348 (2020) - [i161]Kirthevasan Kandasamy, Gur-Eyal Sela, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
Online Learning Demands in Max-min Fairness. CoRR abs/2012.08648 (2020) - [i160]Chris Junchi Li, Michael I. Jordan:
Stochastic Approximation for Online Tensorial Independent Component Analysis. CoRR abs/2012.14415 (2020)
2010 – 2019
- 2019
- [j92]Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht:
First-order methods almost always avoid strict saddle points. Math. Program. 176(1-2): 311-337 (2019) - [j91]Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Transferable Representation Learning with Deep Adaptation Networks. IEEE Trans. Pattern Anal. Mach. Intell. 41(12): 3071-3085 (2019) - [j90]Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan:
Decoding from Pooled Data: Sharp Information-Theoretic Bounds. SIAM J. Math. Data Sci. 1(1): 161-188 (2019) - [j89]Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan:
Decoding From Pooled Data: Phase Transitions of Message Passing. IEEE Trans. Inf. Theory 65(1): 572-585 (2019) - [c286]Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick:
A Swiss Army Infinitesimal Jackknife. AISTATS 2019: 1139-1147 - [c285]Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan:
Probabilistic Multilevel Clustering via Composite Transportation Distance. AISTATS 2019: 3149-3157 - [c284]Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Universal Domain Adaptation. CVPR 2019: 2720-2729 - [c283]Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan:
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data. ICLR (Poster) 2019 - [c282]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. ICML 2019: 3982-3991 - [c281]Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers. ICML 2019: 4013-4022 - [c280]Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML 2019: 4023-4031 - [c279]Michael Muehlebach, Michael I. Jordan:
A Dynamical Systems Perspective on Nesterov Acceleration. ICML 2019: 4656-4662 - [c278]Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan:
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. ICML 2019: 7124-7133 - [c277]Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan:
Bridging Theory and Algorithm for Domain Adaptation. ICML 2019: 7404-7413 - [c276]Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan:
Theoretically Principled Trade-off between Robustness and Accuracy. ICML 2019: 7472-7482 - [c275]Ximei Wang, Ying Jin, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks. NeurIPS 2019: 1951-1961 - [c274]Bin Shi, Simon S. Du, Weijie J. Su, Michael I. Jordan:
Acceleration via Symplectic Discretization of High-Resolution Differential Equations. NeurIPS 2019: 5745-5753 - [i159]Eric Mazumdar, Michael I. Jordan, S. Shankar Sastry:
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games. CoRR abs/1901.00838 (2019) - [i158]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. CoRR abs/1901.06482 (2019) - [i157]Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan:
Theoretically Principled Trade-off between Robustness and Accuracy. CoRR abs/1901.08573 (2019) - [i156]Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu:
Challenges with EM in application to weakly identifiable mixture models. CoRR abs/1902.00194 (2019) - [i155]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. CoRR abs/1902.00618 (2019) - [i154]Xiang Cheng, Peter L. Bartlett, Michael I. Jordan:
Quantitative Central Limit Theorems for Discrete Stochastic Processes. CoRR abs/1902.00832 (2019) - [i153]Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Is There an Analog of Nesterov Acceleration for MCMC? CoRR abs/1902.00996 (2019) - [i152]Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song:
Cost-Effective Incentive Allocation via Structured Counterfactual Inference. CoRR abs/1902.02495 (2019) - [i151]Bin Shi, Simon S. Du, Weijie J. Su, Michael I. Jordan:
Acceleration via Symplectic Discretization of High-Resolution Differential Equations. CoRR abs/1902.03694 (2019) - [i150]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. CoRR abs/1902.03736 (2019) - [i149]Jianbo Chen, Michael I. Jordan:
LS-Tree: Model Interpretation When the Data Are Linguistic. CoRR abs/1902.04187 (2019) - [i148]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
Stochastic Gradient Descent Escapes Saddle Points Efficiently. CoRR abs/1902.04811 (2019) - [i147]Jianbo Chen, Michael I. Jordan:
Boundary Attack++: Query-Efficient Decision-Based Adversarial Attack. CoRR abs/1904.02144 (2019) - [i146]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 G. 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:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i145]Lihua Lei, Michael I. Jordan:
On the Adaptivity of Stochastic Gradient-Based Optimization. CoRR abs/1904.04480 (2019) - [i144]Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan:
Bridging Theory and Algorithm for Domain Adaptation. CoRR abs/1904.05801 (2019) - [i143]Nhat Ho, Tianyi Lin, Michael I. Jordan:
Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and 𝓁1-Convex Clustering. CoRR abs/1904.07462 (2019) - [i142]Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael I. Jordan, Nir Yosef:
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. CoRR abs/1905.02269 (2019) - [i141]Michael Muehlebach, Michael I. Jordan:
A Dynamical Systems Perspective on Nesterov Acceleration. CoRR abs/1905.07436 (2019) - [i140]Wenshuo Guo, Nhat Ho, Michael I. Jordan:
Accelerated Primal-Dual Coordinate Descent for Computational Optimal Transport. CoRR abs/1905.09952 (2019) - [i139]Chiao-Yu Yang, Nhat Ho, Michael I. Jordan:
Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models. CoRR abs/1905.09959 (2019) - [i138]Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett:
Langevin Monte Carlo without Smoothness. CoRR abs/1905.13285 (2019) - [i137]Tianyi Lin, Chi Jin, Michael I. Jordan:
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. CoRR abs/1906.00331 (2019) - [i136]Jelena Diakonikolas, Michael I. Jordan:
Generalized Momentum-Based Methods: A Hamiltonian Perspective. CoRR abs/1906.00436 (2019) - [i135]Tianyi Lin, Nhat Ho, Michael I. Jordan:
On the Acceleration of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport. CoRR abs/1906.01437 (2019) - [i134]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
ML-LOO: Detecting Adversarial Examples with Feature Attribution. CoRR abs/1906.03499 (2019) - [i133]Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan:
Wasserstein Reinforcement Learning. CoRR abs/1906.04349 (2019) - [i132]Lydia T. Liu, Horia Mania, Michael I. Jordan:
Competing Bandits in Matching Markets. CoRR abs/1906.05363 (2019) - [i131]Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan:
Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization. CoRR abs/1907.01127 (2019) - [i130]Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan:
Quantitative W1 Convergence of Langevin-Like Stochastic Processes with Non-Convex Potential State-Dependent Noise. CoRR abs/1907.03215 (2019) - [i129]Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry:
Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings. CoRR abs/1907.03712 (2019) - [i128]Nhat Ho, Chiao-Yu Yang, Michael I. Jordan:
Convergence Rates for Gaussian Mixtures of Experts. CoRR abs/1907.04377 (2019) - [i127]Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Provably Efficient Reinforcement Learning with Linear Function Approximation. CoRR abs/1907.05388 (2019) - [i126]Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, Michael I. Jordan:
Bayesian Robustness: A Nonasymptotic Viewpoint. CoRR abs/1907.11826 (2019) - [i125]Ryan Giordano, Michael I. Jordan, Tamara Broderick:
A Higher-Order Swiss Army Infinitesimal Jackknife. CoRR abs/1907.12116 (2019) - [i124]Kaichao You, Mingsheng Long, Michael I. Jordan, Jianmin Wang:
Learning Stages: Phenomenon, Root Cause, Mechanism Hypothesis, and Implications. CoRR abs/1908.01878 (2019) - [i123]Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. CoRR abs/1908.10859 (2019) - [i122]Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Towards Understanding the Transferability of Deep Representations. CoRR abs/1909.12031 (2019) - [i121]Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan:
On the Complexity of Approximating Multimarginal Optimal Transport. CoRR abs/1910.00152 (2019) - [i120]Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing. CoRR abs/1912.05153 (2019) - [i119]Tianyi Lin, Michael I. Jordan:
A Control-Theoretic Perspective on Optimal High-Order Optimization. CoRR abs/1912.07168 (2019) - 2018
- [j88]Ryan Giordano, Tamara Broderick, Michael I. Jordan:
Covariances, Robustness, and Variational Bayes. J. Mach. Learn. Res. 19: 51:1-51:49 (2018) - [c273]Michael I. Jordan:
Machine learning: trends, perspectives and challenges. TURC 2018: 8 - [c272]Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan:
Underdamped Langevin MCMC: A non-asymptotic analysis. COLT 2018: 300-323 - [c271]Ahmed El Alaoui, Michael I. Jordan:
Detection limits in the high-dimensional spiked rectangular model. COLT 2018: 410-438 - [c270]Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht:
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. COLT 2018: 439-473 - [c269]Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan:
Averaging Stochastic Gradient Descent on Riemannian Manifolds. COLT 2018: 650-687 - [c268]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. COLT 2018: 1042-1085 - [c267]Zhangjie Cao, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Partial Transfer Learning With Selective Adversarial Networks. CVPR 2018: 2724-2732 - [c266]Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. ICML 2018: 763-772 - [c265]Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan:
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. ICML 2018: 882-891 - [c264]Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael I. Jordan, Ion Stoica:
RLlib: Abstractions for Distributed Reinforcement Learning. ICML 2018: 3059-3068 - [c263]Aaditya Ramdas, Tijana Zrnic, Martin J. Wainwright, Michael I. Jordan:
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate. ICML 2018: 4283-4291 - [c262]Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Conditional Adversarial Domain Adaptation. NeurIPS 2018: 1647-1657 - [c261]Shichen Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Generalized Zero-Shot Learning with Deep Calibration Network. NeurIPS 2018: 2009-2019 - [c260]Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan:
Stochastic Cubic Regularization for Fast Nonconvex Optimization. NeurIPS 2018: 2904-2913 - [c259]Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan:
Is Q-Learning Provably Efficient? NeurIPS 2018: 4868-4878 - [c258]Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan:
On the Local Minima of the Empirical Risk. NeurIPS 2018: 4901-4910 - [c257]Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef:
Information Constraints on Auto-Encoding Variational Bayes. NeurIPS 2018: 6117-6128 - [c256]Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. NeurIPS 2018: 7016-7025 - [c255]Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan:
Theoretical guarantees for EM under misspecified Gaussian mixture models. NeurIPS 2018: 9704-9712 - [c254]Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, Ion Stoica:
Ray: A Distributed Framework for Emerging AI Applications. OSDI 2018: 561-577 - [i118]Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. CoRR abs/1802.05431 (2018) - [i117]Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan:
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. CoRR abs/1802.07814 (2018) - [i116]Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht:
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. CoRR abs/1802.08334 (2018) - [i115]Aaditya Ramdas, Tijana Zrnic, Martin J. Wainwright, Michael I. Jordan:
SAFFRON: an adaptive algorithm for online control of the false discovery rate. CoRR abs/1802.09098 (2018) - [i114]Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan:
Averaging Stochastic Gradient Descent on Riemannian Manifolds. CoRR abs/1802.09128 (2018) - [i113]Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I. Jordan, Joseph E. Gonzalez, Sergey Levine:
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning. CoRR abs/1803.00101 (2018) - [i112]Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan:
Minimizing Nonconvex Population Risk from Rough Empirical Risk. CoRR abs/1803.09357 (2018) - [i111]Xiang Cheng, Niladri S. Chatterji, Yasin Abbasi-Yadkori, Peter L. Bartlett, Michael I. Jordan:
Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting. CoRR abs/1805.01648 (2018) - [i110]Romain Lopez, Jeffrey Regier, Nir Yosef, Michael I. Jordan:
Information Constraints on Auto-Encoding Variational Bayes. CoRR abs/1805.08672 (2018) - [i109]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. CoRR abs/1805.12316 (2018) - [i108]Tianyi Lin, Chenyou Fan, Mengdi Wang, Michael I. Jordan:
Improved Oracle Complexity for Stochastic Compositional Variance Reduced Gradient. CoRR abs/1806.00458 (2018) - [i107]Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan:
Is Q-learning Provably Efficient? CoRR abs/1807.03765 (2018) - [i106]Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan:
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data. CoRR abs/1808.02610 (2018) - [i105]Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael I. Jordan, Nir Yosef:
A Deep Generative Model for Semi-Supervised Classification with Noisy Labels. CoRR abs/1809.05957 (2018) - [i104]Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. CoRR abs/1810.04777 (2018) - [i103]Bin Shi, Simon S. Du, Michael I. Jordan, Weijie J. Su:
Understanding the Acceleration Phenomenon via High-Resolution Differential Equations. CoRR abs/1810.08907 (2018) - [i102]Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan:
Probabilistic Multilevel Clustering via Composite Transportation Distance. CoRR abs/1810.11911 (2018) - [i101]Nhat Ho, Tan M. Nguyen, Ankit B. Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk:
Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. CoRR abs/1811.02657 (2018) - [i100]Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation. CoRR abs/1811.08393 (2018) - [i99]Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan:
Sampling Can Be Faster Than Optimization. CoRR abs/1811.08413 (2018) - [i98]Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan:
Asynchronous Online Testing of Multiple Hypotheses. CoRR abs/1812.05068 (2018) - 2017
- [j87]Nicholas Boyd, Trevor Hastie, Stephen P. Boyd, Benjamin Recht, Michael I. Jordan:
Saturating Splines and Feature Selection. J. Mach. Learn. Res. 18: 197:1-197:32 (2017) - [j86]Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi:
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. J. Mach. Learn. Res. 18: 230:1-230:49 (2017) - [j85]Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác:
Distributed optimization with arbitrary local solvers. Optim. Methods Softw. 32(4): 813-848 (2017) - [j84]Horia Mania, Xinghao Pan, Dimitris S. Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization. SIAM J. Optim. 27(4): 2202-2229 (2017) - [j83]Sindhu Ghanta, Michael I. Jordan, Kivanç Köse, Dana H. Brooks, Milind Rajadhyaksha, Jennifer G. Dy:
A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin. IEEE Trans. Image Process. 26(1): 172-184 (2017) - [c253]Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan:
On the Learnability of Fully-Connected Neural Networks. AISTATS 2017: 83-91 - [c252]Lihua Lei, Michael I. Jordan:
Less than a Single Pass: Stochastically Controlled Stochastic Gradient. AISTATS 2017: 148-156 - [c251]Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan:
QuTE: Decentralized multiple testing on sensor networks with false discovery rate control. CDC 2017: 6415-6421 - [c250]Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael I. Jordan, Ion Stoica:
Real-Time Machine Learning: The Missing Pieces. HotOS 2017: 106-110 - [c249]Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan:
How to Escape Saddle Points Efficiently. ICML 2017: 1724-1732 - [c248]Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan:
Deep Transfer Learning with Joint Adaptation Networks. ICML 2017: 2208-2217 - [c247]Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht:
Breaking Locality Accelerates Block Gauss-Seidel. ICML 2017: 3482-3491 - [c246]Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan:
Decoding from pooled data: Phase transitions of message passing. ISIT 2017: 2780-2784 - [c245]Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabás Póczos:
Gradient Descent Can Take Exponential Time to Escape Saddle Points. NIPS 2017: 1067-1077 - [c244]Lihua Lei, Cheng Ju, Jianbo Chen, Michael I. Jordan:
Non-convex Finite-Sum Optimization Via SCSG Methods. NIPS 2017: 2348-2358 - [c243]Jeffrey Regier, Michael I. Jordan, Jon McAuliffe:
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. NIPS 2017: 2402-2411 - [c242]Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan:
Online control of the false discovery rate with decaying memory. NIPS 2017: 5650-5659 - [c241]Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan:
Kernel Feature Selection via Conditional Covariance Minimization. NIPS 2017: 6946-6955 - [c240]Michael I. Jordan:
On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic. SIGMETRICS (Abstracts) 2017: 58 - [i97]Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan:
Decoding from Pooled Data: Phase Transitions of Message Passing. CoRR abs/1702.02279 (2017) - [i96]Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan:
How to Escape Saddle Points Efficiently. CoRR abs/1703.00887 (2017) - [i95]Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Michael I. Jordan, Ion Stoica:
Real-Time Machine Learning: The Missing Pieces. CoRR abs/1703.03924 (2017) - [i94]Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Barnabás Póczos, Aarti Singh:
Gradient Descent Can Take Exponential Time to Escape Saddle Points. CoRR abs/1705.10412 (2017) - [i93]Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan:
Domain Adaptation with Randomized Multilinear Adversarial Networks. CoRR abs/1705.10667 (2017) - [i92]Jeffrey Regier, Michael I. Jordan, Jon McAuliffe:
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. CoRR abs/1706.02375 (2017) - [i91]Lihua Lei, Cheng Ju, Jianbo Chen, Michael I. Jordan:
Nonconvex Finite-Sum Optimization Via SCSG Methods. CoRR abs/1706.09156 (2017) - [i90]Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan:
Kernel Feature Selection via Conditional Covariance Minimization. CoRR abs/1707.01164 (2017) - [i89]Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan:
Underdamped Langevin MCMC: A non-asymptotic analysis. CoRR abs/1707.03663 (2017) - [i88]Zhangjie Cao, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Partial Transfer Learning with Selective Adversarial Networks. CoRR abs/1707.07901 (2017) - [i87]Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef:
A deep generative model for gene expression profiles from single-cell RNA sequencing. CoRR abs/1709.02082 (2017) - [i86]Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan:
DAGGER: A sequential algorithm for FDR control on DAGs. CoRR abs/1709.10250 (2017) - [i85]Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan:
Online control of the false discovery rate with decaying memory. CoRR abs/1710.00499 (2017) - [i84]Ahmed El Alaoui, Florent Krzakala, Michael I. Jordan:
Finite Size Corrections and Likelihood Ratio Fluctuations in the Spiked Wigner Model. CoRR abs/1710.02903 (2017) - [i83]Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht:
First-order Methods Almost Always Avoid Saddle Points. CoRR abs/1710.07406 (2017) - [i82]Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan:
Stochastic Cubic Regularization for Fast Nonconvex Optimization. CoRR abs/1711.02838 (2017) - [i81]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. CoRR abs/1711.10456 (2017) - [i80]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:
A Berkeley View of Systems Challenges for AI. CoRR abs/1712.05855 (2017) - [i79]Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, William Paul, Michael I. Jordan, Ion Stoica:
Ray: A Distributed Framework for Emerging AI Applications. CoRR abs/1712.05889 (2017) - 2016
- [j82]Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan:
Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing. J. Mach. Learn. Res. 17: 102:1-102:44 (2016) - [c239]Feiping Nie, Xiaoqian Wang, Michael I. Jordan, Heng Huang:
The Constrained Laplacian Rank Algorithm for Graph-Based Clustering. AAAI 2016: 1969-1976 - [c238]Philipp Moritz, Robert Nishihara, Michael I. Jordan:
A Linearly-Convergent Stochastic L-BFGS Algorithm. AISTATS 2016: 249-258 - [c237]Jason D. Lee, Max Simchowitz, Michael I. Jordan, Benjamin Recht:
Gradient Descent Only Converges to Minimizers. COLT 2016: 1246-1257 - [c236]Qiang Liu, Jason D. Lee, Michael I. Jordan:
A Kernelized Stein Discrepancy for Goodness-of-fit Tests. ICML 2016: 276-284 - [c235]Yuchen Zhang, Jason D. Lee, Michael I. Jordan:
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time. ICML 2016: 993-1001 - [c234]Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan:
Unsupervised Domain Adaptation with Residual Transfer Networks. NIPS 2016: 136-144 - [c233]Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan:
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. NIPS 2016: 4116-4124 - [c232]Michael I. Jordan:
On Computational Thinking, Inferential Thinking and Data Science. SPAA 2016: 47 - [c231]Philipp Moritz, Robert Nishihara, Ion Stoica, Michael I. Jordan:
SparkNet: Training Deep Networks in Spark. ICLR (Poster) 2016 - [c230]John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel:
High-Dimensional Continuous Control Using Generalized Advantage Estimation. ICLR (Poster) 2016 - [i78]Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Unsupervised Domain Adaptation with Residual Transfer Networks. CoRR abs/1602.04433 (2016) - [i77]Jason D. Lee, Max Simchowitz, Michael I. Jordan, Benjamin Recht:
Gradient Descent Converges to Minimizers. CoRR abs/1602.04915 (2016) - [i76]Ahmed El Alaoui, Xiang Cheng, Aaditya Ramdas, Martin J. Wainwright, Michael I. Jordan:
Asymptotic behavior of ℓp-based Laplacian regularization in semi-supervised learning. CoRR abs/1603.00564 (2016) - [i75]Andre Wibisono, Ashia C. Wilson, Michael I. Jordan:
A Variational Perspective on Accelerated Methods in Optimization. CoRR abs/1603.04245 (2016) - [i74]Horia Mania, Aaditya Ramdas, Martin J. Wainwright, Michael I. Jordan, Benjamin Recht:
Universality of Mallows' and degeneracy of Kendall's kernels for rankings. CoRR abs/1603.08035 (2016) - [i73]John C. Duchi, Martin J. Wainwright, Michael I. Jordan:
Minimax Optimal Procedures for Locally Private Estimation. CoRR abs/1604.02390 (2016) - [i72]Maxim Rabinovich, Aaditya Ramdas, Michael I. Jordan, Martin J. Wainwright:
Function-Specific Mixing Times and Concentration Away from Equilibrium. CoRR abs/1605.02077 (2016) - [i71]Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Deep Transfer Learning with Joint Adaptation Networks. CoRR abs/1605.06636 (2016) - [i70]Michael I. Jordan, Jason D. Lee, Yun Yang:
Communication-efficient distributed statistical learning. CoRR abs/1605.07689 (2016) - [i69]Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris S. Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, Benjamin Recht:
CYCLADES: Conflict-free Asynchronous Machine Learning. CoRR abs/1605.09721 (2016) - [i68]Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan:
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. CoRR abs/1609.00978 (2016) - [i67]Lihua Lei, Michael I. Jordan:
Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method. CoRR abs/1609.03261 (2016) - [i66]Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi:
CoCoA: A General Framework for Communication-Efficient Distributed Optimization. CoRR abs/1611.02189 (2016) - [i65]Ashia C. Wilson, Benjamin Recht, Michael I. Jordan:
A Lyapunov Analysis of Momentum Methods in Optimization. CoRR abs/1611.02635 (2016) - [i64]Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan:
Decoding from Pooled Data: Sharp Information-Theoretic Bounds. CoRR abs/1611.09981 (2016) - 2015
- [j81]Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan:
Distributed matrix completion and robust factorization. J. Mach. Learn. Res. 16: 913-960 (2015) - [j80]John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan:
Nested Hierarchical Dirichlet Processes. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 256-270 (2015) - [j79]Tamara Broderick, Lester W. Mackey, John W. Paisley, Michael I. Jordan:
Combinatorial Clustering and the Beta Negative Binomial Process. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 290-306 (2015) - [j78]John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono:
Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations. IEEE Trans. Inf. Theory 61(5): 2788-2806 (2015) - [c229]Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan:
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CIDR 2015 - [c228]Evan Randall Sparks, Ameet Talwalkar, Daniel Haas, Michael J. Franklin, Michael I. Jordan, Tim Kraska:
Automating model search for large scale machine learning. SoCC 2015: 368-380 - [c227]Mingsheng Long, Yue Cao, Jianmin Wang, Michael I. Jordan:
Learning Transferable Features with Deep Adaptation Networks. ICML 2015: 97-105 - [c226]Robert Nishihara, Laurent Lessard, Benjamin Recht, Andrew K. Packard, Michael I. Jordan:
A General Analysis of the Convergence of ADMM. ICML 2015: 343-352 - [c225]Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan:
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds. ICML 2015: 457-465 - [c224]John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, Philipp Moritz:
Trust Region Policy Optimization. ICML 2015: 1889-1897 - [c223]Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác:
Adding vs. Averaging in Distributed Primal-Dual Optimization. ICML 2015: 1973-1982 - [c222]Teodor Mihai Moldovan, Sergey Levine, Michael I. Jordan, Pieter Abbeel:
Optimism-driven exploration for nonlinear systems. ICRA 2015: 3239-3246 - [c221]Xinghao Pan, Dimitris S. Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Parallel Correlation Clustering on Big Graphs. NIPS 2015: 82-90 - [c220]Maxim Rabinovich, Elaine Angelino, Michael I. Jordan:
Variational Consensus Monte Carlo. NIPS 2015: 1207-1215 - [c219]Ryan Giordano, Tamara Broderick, Michael I. Jordan:
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes. NIPS 2015: 1441-1449 - [c218]Jacob Andreas, Maxim Rabinovich, Michael I. Jordan, Dan Klein:
On the Accuracy of Self-Normalized Log-Linear Models. NIPS 2015: 1783-1791 - [c217]Michael I. Jordan:
Computational Thinking, Inferential Thinking and "Big Data". PODS 2015: 1 - [c216]Christopher Ré, Divy Agrawal, Magdalena Balazinska, Michael J. Cafarella, Michael I. Jordan, Tim Kraska, Raghu Ramakrishnan:
Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype? SIGMOD Conference 2015: 283-284 - [i63]Evan Randall Sparks, Ameet Talwalkar, Michael J. Franklin, Michael I. Jordan, Tim Kraska:
TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries. CoRR abs/1502.00068 (2015) - [i62]Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan:
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds. CoRR abs/1502.01403 (2015) - [i61]Robert Nishihara, Laurent Lessard, Benjamin Recht, Andrew K. Packard, Michael I. Jordan:
A General Analysis of the Convergence of ADMM. CoRR abs/1502.02009 (2015) - [i60]Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác:
Adding vs. Averaging in Distributed Primal-Dual Optimization. CoRR abs/1502.03508 (2015) - [i59]John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel:
Trust Region Policy Optimization. CoRR abs/1502.05477 (2015) - [i58]Yun Yang, Martin J. Wainwright, Michael I. Jordan:
On the Computational Complexity of High-Dimensional Bayesian Variable Selection. CoRR abs/1505.07925 (2015) - [i57]Jacob Andreas, Maxim Rabinovich, Dan Klein, Michael I. Jordan:
On the accuracy of self-normalized log-linear models. CoRR abs/1506.04147 (2015) - [i56]Yuchen Zhang, Michael I. Jordan:
Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms. CoRR abs/1506.07552 (2015) - [i55]Xinghao Pan, Dimitris S. Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Parallel Correlation Clustering on Big Graphs. CoRR abs/1507.05086 (2015) - [i54]Horia Mania, Xinghao Pan, Dimitris S. Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization. CoRR abs/1507.06970 (2015) - [i53]Philipp Moritz, Robert Nishihara, Michael I. Jordan:
A Linearly-Convergent Stochastic L-BFGS Algorithm. CoRR abs/1508.02087 (2015) - [i52]Yuchen Zhang, Jason D. Lee, Michael I. Jordan:
ℓ1-regularized Neural Networks are Improperly Learnable in Polynomial Time. CoRR abs/1510.03528 (2015) - [i51]Joseph E. Gonzalez, Peter Bailis, Michael I. Jordan, Michael J. Franklin, Joseph M. Hellerstein, Ali Ghodsi, Ion Stoica:
Asynchronous Complex Analytics in a Distributed Dataflow Architecture. CoRR abs/1510.07092 (2015) - [i50]Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan:
Learning Halfspaces and Neural Networks with Random Initialization. CoRR abs/1511.07948 (2015) - [i49]Virginia Smith, Simone Forte, Michael I. Jordan, Martin Jaggi:
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework. CoRR abs/1512.04011 (2015) - [i48]Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác:
Distributed Optimization with Arbitrary Local Solvers. CoRR abs/1512.04039 (2015) - 2014
- [j77]Ameet Talwalkar, Jesse Liptrap, Julie Newcomb, Christopher Hartl, Jonathan Terhorst, Kristal Curtis, Ma'ayan Bresler, Yun S. Song, Michael I. Jordan, David A. Patterson:
SMaSH: a benchmarking toolkit for human genome variant calling. Bioinform. 30(19): 2787-2795 (2014) - [j76]John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Privacy Aware Learning. J. ACM 61(6): 38:1-38:57 (2014) - [j75]Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön:
Particle gibbs with ancestor sampling. J. Mach. Learn. Res. 15(1): 2145-2184 (2014) - [j74]Donglin Niu, Jennifer G. Dy, Michael I. Jordan:
Iterative Discovery of Multiple AlternativeClustering Views. IEEE Trans. Pattern Anal. Mach. Intell. 36(7): 1340-1353 (2014) - [j73]Barzan Mozafari, Purnamrita Sarkar, Michael J. Franklin, Michael I. Jordan, Samuel Madden:
Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning. Proc. VLDB Endow. 8(2): 125-136 (2014) - [c215]Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan:
Lower bounds on the performance of polynomial-time algorithms for sparse linear regression. COLT 2014: 921-948 - [c214]Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, Michael I. Jordan:
Parallel Double Greedy Submodular Maximization. NIPS 2014: 118-126 - [c213]Robert Nishihara, Stefanie Jegelka, Michael I. Jordan:
On the Convergence Rate of Decomposable Submodular Function Minimization. NIPS 2014: 640-648 - [c212]Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan:
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing. NIPS 2014: 1260-1268 - [c211]Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. NIPS 2014: 3068-3076 - [c210]Adam E. Bloniarz, Ameet Talwalkar, Jonathan Terhorst, Michael I. Jordan, David A. Patterson, Bin Yu, Yun S. Song:
Changepoint Analysis for Efficient Variant Calling. RECOMB 2014: 20-34 - [c209]Sameer Agarwal, Henry Milner, Ariel Kleiner, Ameet Talwalkar, Michael I. Jordan, Samuel Madden, Barzan Mozafari, Ion Stoica:
Knowing when you're wrong: building fast and reliable approximate query processing systems. SIGMOD Conference 2014: 481-492 - [p5]Michael I. Jordan, Christopher M. Bishop:
Neural Networks. Computing Handbook, 3rd ed. (1) 2014: 42: 1-24 - [r4]John W. Paisley, David M. Blei, Michael I. Jordan:
Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference. Handbook of Mixed Membership Models and Their Applications 2014: 205-224 - [r3]Lester W. Mackey, David J. Weiss, Michael I. Jordan:
Mixed Membership Matrix Factorization. Handbook of Mixed Membership Models and Their Applications 2014: 351-367 - [r2]Emily B. Fox, Michael I. Jordan:
Mixed Membership Models for Time Series. Handbook of Mixed Membership Models and Their Applications 2014: 417-439 - [i47]Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan:
Lower bounds on the performance of polynomial-time algorithms for sparse linear regression. CoRR abs/1402.1918 (2014) - [i46]John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Yuchen Zhang:
Information-theoretic lower bounds for distributed statistical estimation with communication constraints. CoRR abs/1405.0782 (2014) - [i45]Robert Nishihara, Stefanie Jegelka, Michael I. Jordan:
On the Convergence Rate of Decomposable Submodular Function Minimization. CoRR abs/1406.6474 (2014) - [i44]Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan:
Communication-Efficient Distributed Dual Coordinate Ascent. CoRR abs/1409.1458 (2014) - [i43]Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan:
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CoRR abs/1409.3809 (2014) - 2013
- [j72]Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan:
Bayesian semiparametric Wiener system identification. Autom. 49(7): 2053-2063 (2013) - [j71]Percy Liang, Michael I. Jordan, Dan Klein:
Learning Dependency-Based Compositional Semantics. Comput. Linguistics 39(2): 389-446 (2013) - [j70]Donghui Yan, Aiyou Chen, Michael I. Jordan:
Cluster Forests. Comput. Stat. Data Anal. 66: 178-192 (2013) - [c208]John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Local privacy and statistical minimax rates. Allerton 2013: 1592 - [c207]Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan:
MLbase: A Distributed Machine-learning System. CIDR 2013 - [c206]John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Local Privacy and Statistical Minimax Rates. FOCS 2013: 429-438 - [c205]Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan:
Distributed Low-Rank Subspace Segmentation. ICCV 2013: 3543-3550 - [c204]Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska:
MLI: An API for Distributed Machine Learning. ICDM 2013: 1187-1192 - [c203]Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic:
Efficient Ranking from Pairwise Comparisons. ICML (3) 2013: 109-117 - [c202]Tamara Broderick, Brian Kulis, Michael I. Jordan:
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. ICML (3) 2013: 226-234 - [c201]Ariel Kleiner, Ameet Talwalkar, Sameer Agarwal, Ion Stoica, Michael I. Jordan:
A general bootstrap performance diagnostic. KDD 2013: 419-427 - [c200]Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan:
A Comparative Framework for Preconditioned Lasso Algorithms. NIPS 2013: 1061-1069 - [c199]Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan:
Optimistic Concurrency Control for Distributed Unsupervised Learning. NIPS 2013: 1403-1411 - [c198]John C. Duchi, Martin J. Wainwright, Michael I. Jordan:
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation. NIPS 2013: 1529-1537 - [c197]Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan:
Streaming Variational Bayes. NIPS 2013: 1727-1735 - [c196]Yuchen Zhang, John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Information-theoretic lower bounds for distributed statistical estimation with communication constraints. NIPS 2013: 2328-2336 - [c195]John C. Duchi, Michael I. Jordan, H. Brendan McMahan:
Estimation, Optimization, and Parallelism when Data is Sparse. NIPS 2013: 2832-2840 - [c194]John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan:
A Nested HDP for Hierarchical Topic Models. ICLR (Workshop) 2013 - [i42]Francis R. Bach, Michael I. Jordan:
Tree-dependent Component Analysis. CoRR abs/1301.0554 (2013) - [i41]Sekhar Tatikonda, Michael I. Jordan:
Loopy Belief Propogation and Gibbs Measures. CoRR abs/1301.0605 (2013) - [i40]Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Michael I. Jordan, Stuart Russell:
Variational MCMC. CoRR abs/1301.2266 (2013) - [i39]Amol Deshpande, Minos N. Garofalakis, Michael I. Jordan:
Efficient Stepwise Selection in Decomposable Models. CoRR abs/1301.2267 (2013) - [i38]Andrew Y. Ng, Michael I. Jordan:
PEGASUS: A Policy Search Method for Large MDPs and POMDPs. CoRR abs/1301.3878 (2013) - [i37]Kevin P. Murphy, Yair Weiss, Michael I. Jordan:
Loopy Belief Propagation for Approximate Inference: An Empirical Study. CoRR abs/1301.6725 (2013) - [i36]Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan:
Mixture Representations for Inference and Learning in Boltzmann Machines. CoRR abs/1301.7393 (2013) - [i35]John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Local Privacy and Statistical Minimax Rates. CoRR abs/1302.3203 (2013) - [i34]Tommi S. Jaakkola, Michael I. Jordan:
Computing Upper and Lower Bounds on Likelihoods in Intractable Networks. CoRR abs/1302.3586 (2013) - [i33]Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan:
Divide-and-Conquer Subspace Segmentation. CoRR abs/1304.5583 (2013) - [i32]John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation. CoRR abs/1305.6000 (2013) - [i31]Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan:
Streaming Variational Bayes. CoRR abs/1307.6769 (2013) - [i30]Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan:
Optimistic Concurrency Control for Distributed Unsupervised Learning. CoRR abs/1307.8049 (2013) - [i29]Emily B. Fox, Michael I. Jordan:
Mixed Membership Models for Time Series. CoRR abs/1309.3533 (2013) - [i28]Michael I. Jordan:
On statistics, computation and scalability. CoRR abs/1309.7804 (2013) - [i27]Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska:
MLI: An API for Distributed Machine Learning. CoRR abs/1310.5426 (2013) - [i26]John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono:
Optimal rates for zero-order optimization: the power of two function evaluations. CoRR abs/1312.2139 (2013) - 2012
- [j69]Zhihua Zhang, Shusen Wang, Dehua Liu, Michael I. Jordan:
EP-GIG Priors and Applications in Bayesian Sparse Learning. J. Mach. Learn. Res. 13: 2031-2061 (2012) - [j68]Zhihua Zhang, Dehua Liu, Guang Dai, Michael I. Jordan:
Coherence functions with applications in large-margin classification methods. J. Mach. Learn. Res. 13: 2705-2734 (2012) - [j67]Paul Lukowicz, Sanjiv Nanda, Vidya Narayanan, Hal Abelson, Deborah L. McGuinness, Michael I. Jordan:
Qualcomm Context-Awareness Symposium Sets Research Agenda for Context-Aware Smartphones. IEEE Pervasive Comput. 11(1): 76-79 (2012) - [j66]John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan:
Ergodic Mirror Descent. SIAM J. Optim. 22(4): 1549-1578 (2012) - [c193]Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan:
The Big Data Bootstrap. ICML 2012 - [c192]Brian Kulis, Michael I. Jordan:
Revisiting k-means: New Algorithms via Bayesian Nonparametrics. ICML 2012 - [c191]John W. Paisley, David M. Blei, Michael I. Jordan:
Variational Bayesian Inference with Stochastic Search. ICML 2012 - [c190]Purnamrita Sarkar, Deepayan Chakrabarti, Michael I. Jordan:
Nonparametric Link Prediction in Dynamic Networks. ICML 2012 - [c189]Michael I. Jordan:
Divide-and-conquer and statistical inference for big data. KDD 2012: 4 - [c188]Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan:
Active spectral clustering via iterative uncertainty reduction. KDD 2012: 1339-1347 - [c187]John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Privacy Aware Learning. NIPS 2012: 1439-1447 - [c186]John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono:
Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods. NIPS 2012: 1448-1456 - [c185]Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön:
Ancestor Sampling for Particle Gibbs. NIPS 2012: 2600-2608 - [c184]Ke Jiang, Brian Kulis, Michael I. Jordan:
Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. NIPS 2012: 3167-3175 - [c183]John W. Paisley, David M. Blei, Michael I. Jordan:
Stick-Breaking Beta Processes and the Poisson Process. AISTATS 2012: 850-858 - [i25]Aleksandr Simma, Michael I. Jordan:
Modeling Events with Cascades of Poisson Processes. CoRR abs/1203.3516 (2012) - [i24]John C. Duchi, Lester W. Mackey, Michael I. Jordan:
The Asymptotics of Ranking Algorithms. CoRR abs/1204.1688 (2012) - [i23]Alexandre Bouchard-Côté, Michael I. Jordan:
Optimization of Structured Mean Field Objectives. CoRR abs/1205.2658 (2012) - [i22]Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan:
The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. CoRR abs/1206.3279 (2012) - [i21]Zhihua Zhang, Michael I. Jordan:
Bayesian Multicategory Support Vector Machines. CoRR abs/1206.6863 (2012) - [i20]Michal Rosen-Zvi, Michael I. Jordan, Alan L. Yuille:
The DLR Hierarchy of Approximate Inference. CoRR abs/1207.1417 (2012) - [i19]