| 2026 | COLT | Calibeating Made Simple. | Yurong Chen, Zhiyi Huang, Michael I. Jordan, Haipeng Luo |
| 2026 | COLT | Blackwell Approachability and Gradient Equilibrium are Equivalent. | Brian W. Lee, Nika Haghtalab, Michael I. Jordan, Ryan J. Tibshirani |
| 2025 | AISTATS | Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy. | Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas A. Courtade, Alireza Fallah, Michael I. Jordan |
| 2025 | AISTATS | Automatically Adaptive Conformal Risk Control. | Vincent Blot, Anastasios Nikolas Angelopoulos, Michael I. Jordan, Nicolas J.-B. Brunel |
| 2025 | ICML | AutoEval Done Right: Using Synthetic Data for Model Evaluation. | Pierre Boyeau, Anastasios Nikolas Angelopoulos, Tianle Li, Nir Yosef, Jitendra Malik, Michael I. Jordan |
| 2025 | ICML | Prediction-Aware Learning in Multi-Agent Systems. | Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Oliviero Durmus |
| 2025 | ICML | Statistical Collusion by Collectives on Learning Platforms. | Etienne Gauthier, Francis Bach, Michael I. Jordan |
| 2025 | WWW | Relying on the Metrics of Evaluated Agents. | Serena Wang, Michael I. Jordan, Katrina Ligett, R. Preston McAfee |
| 2024 | AISTATS | Delegating Data Collection in Decentralized Machine Learning. | Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab |
| 2024 | AISTATS | Classifier Calibration with ROC-Regularized Isotonic Regression. | Eugene Berta, Francis R. Bach, Michael I. Jordan |
| 2024 | AISTATS | A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. | Tianyi Lin, Marco Cuturi, Michael I. Jordan |
| 2024 | AISTATS | On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry. | Serena Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan |
| 2024 | ICLR | A Primal-Dual Approach to Solving Variational Inequalities with General Constraints. | Tatjana Chavdarova, Tong Yang, Matteo Pagliardini, Michael I. Jordan |
| 2024 | ICML | Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. | Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan |
| 2024 | ICML | Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. | Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael I. Jordan, Joseph E. Gonzalez, Ion Stoica |
| 2024 | ICML | Incentivized Learning in Principal-Agent Bandit Games. | Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus |
| 2024 | ICML | Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. | Banghua Zhu, Michael I. Jordan, Jiantao Jiao |
| 2024 | ICRA | Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions. | Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik |
| 2023 | AAAI | Competition, Alignment, and Equilibria in Digital Marketplaces. | Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab |
| 2023 | AISTATS | A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. | Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan |
| 2023 | AISTATS | Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning. | Ruitu Xu, Yifei Min, Tianhao Wang, Michael I. Jordan, Zhaoran Wang, Zhuoran Yang |
| 2023 | AISTATS | Byzantine-Robust Federated Learning with Optimal Statistical Rates. | Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan |
| 2023 | ALT | An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. | Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan |
| 2023 | COLT | Deterministic Nonsmooth Nonconvex Optimization. | Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis |
| 2023 | CVPR | Neural Dependencies Emerging from Learning Massive Categories. | Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael I. Jordan, Zheng-Jun Zha |
| 2023 | ICLR | A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. | Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan |
| 2023 | ICLR | Modeling content creator incentives on algorithm-curated platforms. | Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean |
| 2023 | ICLR | Solving Constrained Variational Inequalities via a First-order Interior Point-based Method. | Tong Yang, Michael I. Jordan, Tatjana Chavdarova |
| 2023 | ICML | Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. | Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan |
| 2023 | ICML | Federated Conformal Predictors for Distributed Uncertainty Quantification. | Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar |
| 2023 | ICML | Online Learning in Stackelberg Games with an Omniscient Follower. | Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan |
| 2023 | ICML | Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. | Banghua Zhu, Michael I. Jordan, Jiantao Jiao |
| 2023 | KDD | KDD-2023 Workshop on Decision Intelligence and Analytics for Online Marketplaces. | Zhiwei (Tony) Qin, Rui Song, Jieping Ye, Hongtu Zhu, Michael I. Jordan |
| 2023 | OSDI | Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback. | Romil Bhardwaj, Kirthevasan Kandasamy, Asim Biswal, Wenshuo Guo, Benjamin Hindman, Joseph Gonzalez, Michael I. Jordan, Ion Stoica |
| 2023 | UAI | Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. | Chris Junchi Li, Michael I. Jordan |
| 2022 | AISTATS | Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. | Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica |
| 2022 | AISTATS | On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. | Nhat Ho, Tianyi Lin, Michael I. Jordan |
| 2022 | AISTATS | On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. | Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan |
| 2022 | AISTATS | Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. | Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan |
| 2022 | COLT | Optimal Mean Estimation without a Variance. | Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan |
| 2022 | COLT | ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. | Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan |
| 2022 | ICML | Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging. | Anastasios N. Angelopoulos, Amit Pal Singh Kohli, Stephen Bates, Michael I. Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano |
| 2022 | ICML | No-Regret Learning in Partially-Informed Auctions. | Wenshuo Guo, Michael I. Jordan, Ellen Vitercik |
| 2022 | ICML | Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. | Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan |
| 2022 | ICML | Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy. | Zhihan Liu, Miao Lu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang |
| 2022 | ISIT | Robust Estimation for Non-parametric Families via Generative Adversarial Networks. | Banghua Zhu, Jiantao Jiao, Michael I. Jordan |
| 2022 | KDD | Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail and Beyond. | Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan |
| 2022 | KDD | The 5th Artificial Intelligence of Things (AIoT) Workshop. | Jian Zhang, Jian Tang, Yiran Chen, Jie Liu, Jieping Ye, Marilyn Wolf, Vijaykrishnan Narayanan, Mani B. Srivastava, Michael I. Jordan, Victor Bahl |
| 2022 | RECOMB | Identifying Systematic Variation at the Single-Cell Level by Leveraging Low-Resolution Population-Level Data. | Elior Rahmani, Michael I. Jordan, Nir Yosef |
| 2021 | AAAI | Learning from eXtreme Bandit Feedback. | Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan |
| 2021 | AAAI | Robustness Guarantees for Mode Estimation with an Application to Bandits. | Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan |
| 2021 | AISTATS | Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. | Jelena Diakonikolas, Constantinos Daskalakis, Michael I. Jordan |
| 2021 | AISTATS | On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. | Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, Michael I. Jordan |
| 2021 | CLOUD | Elastic Hyperparameter Tuning on the Cloud. | Lisa Dunlap, Kirthevasan Kandasamy, Ujval Misra, Richard Liaw, Michael I. Jordan, Ion Stoica, Joseph E. Gonzalez |
| 2021 | COLT | Stochastic Approximation for Online Tensorial Independent Component Analysis. | Chris Junchi Li, Michael I. Jordan |
| 2021 | ICLR | Uncertainty Sets for Image Classifiers using Conformal Prediction. | Anastasios Nikolas Angelopoulos, Stephen Bates, Michael I. Jordan, Jitendra Malik |
| 2021 | ICML | Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. | Esther Rolf, Theodora T. Worledge, Benjamin Recht, Michael I. Jordan |
| 2021 | ICML | Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism. | Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph Gonzalez |
| 2021 | ICML | Provable Meta-Learning of Linear Representations. | Nilesh Tripuraneni, Chi Jin, Michael I. Jordan |
| 2021 | KDD | The 4th Artificial Intelligence of Things (AIoT) Workshop. | Jian Zhang, Jian Tang, Yiran Chen, Jie Liu, Jieping Ye, Marilyn Wolf, Vijaykrishnan Narayanan, Mani Srivastava, Michael I. Jordan, Victor Bahl |
| 2021 | UAI | Variational refinement for importance sampling using the forward Kullback-Leibler divergence. | Ghassen Jerfel, Serena Lutong Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan |
| 2020 | AAAI | LS-Tree: Model Interpretation When the Data Are Linguistic. | Jianbo Chen, Michael I. Jordan |
| 2020 | AAAI | Cost-Effective Incentive Allocation via Structured Counterfactual Inference. | Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song |
| 2020 | AAAI | ML-LOO: Detecting Adversarial Examples with Feature Attribution. | Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan |
| 2020 | AISTATS | Langevin Monte Carlo without smoothness. | Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett |
| 2020 | AISTATS | Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models. | Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu |
| 2020 | AISTATS | Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter. | Wenshuo Guo, Nhat Ho, Michael I. Jordan |
| 2020 | AISTATS | Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. | Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan |
| 2020 | AISTATS | Competing Bandits in Matching Markets. | Lydia T. Liu, Horia Mania, Michael I. Jordan |
| 2020 | AISTATS | Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. | Esther Rolf, Michael I. Jordan, Benjamin Recht |
| 2020 | AISTATS | The Power of Batching in Multiple Hypothesis Testing. | Tijana Zrnic, Daniel L. Jiang, Aaditya Ramdas, Michael I. Jordan |
| 2020 | COLT | Provably efficient reinforcement learning with linear function approximation. | Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan |
| 2020 | COLT | Near-Optimal Algorithms for Minimax Optimization. | Tianyi Lin, Chi Jin, Michael I. Jordan |
| 2020 | COLT | On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. | Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan |
| 2020 | ICLR | Variance Reduction With Sparse Gradients. | Melih Elibol, Lihua Lei, Michael I. Jordan |
| 2020 | ICML | Stochastic Gradient and Langevin Processes. | Xiang Cheng, Dong Yin, Peter L. Bartlett, Michael I. Jordan |
| 2020 | ICML | What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? | Chi Jin, Praneeth Netrapalli, Michael I. Jordan |
| 2020 | ICML | Accelerated Message Passing for Entropy-Regularized MAP Inference. | Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan |
| 2020 | ICML | On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. | Tianyi Lin, Chi Jin, Michael I. Jordan |
| 2020 | ICML | Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. | Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan |
| 2020 | ICML | On Approximate Thompson Sampling with Langevin Algorithms. | Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett |
| 2020 | ICML | Continuous-time Lower Bounds for Gradient-based Algorithms. | Michael Muehlebach, Michael I. Jordan |
| 2020 | ICML | Learning to Score Behaviors for Guided Policy Optimization. | Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan |
| 2020 | SP | HopSkipJumpAttack: A Query-Efficient Decision-Based Attack. | Jianbo Chen, Michael I. Jordan, Martin J. Wainwright |
| 2019 | AISTATS | A Swiss Army Infinitesimal Jackknife. | Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick |
| 2019 | AISTATS | Probabilistic Multilevel Clustering via Composite Transportation Distance. | Nhat Ho, Viet Huynh, Dinh Q. Phung, Michael I. Jordan |
| 2019 | CVPR | Universal Domain Adaptation. | Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan |
| 2019 | ICLR | L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data. | Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan |
| 2019 | ICML | Bridging Theory and Algorithm for Domain Adaptation. | Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan |
| 2019 | ICML | On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. | Tianyi Lin, Nhat Ho, Michael I. Jordan |
| 2019 | ICML | Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers. | Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan |
| 2019 | ICML | Rao-Blackwellized Stochastic Gradients for Discrete Distributions. | Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe |
| 2019 | ICML | A Dynamical Systems Perspective on Nesterov Acceleration. | Michael Muehlebach, Michael I. Jordan |
| 2019 | ICML | Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. | Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan |
| 2019 | ICML | Theoretically Principled Trade-off between Robustness and Accuracy. | Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan |
| 2018 | COLT | Detection limits in the high-dimensional spiked rectangular model. | Ahmed El Alaoui, Michael I. Jordan |
| 2018 | COLT | Underdamped Langevin MCMC: A non-asymptotic analysis. | Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, Michael I. Jordan |
| 2018 | COLT | Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. | Chi Jin, Praneeth Netrapalli, Michael I. Jordan |
| 2018 | COLT | Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. | Max Simchowitz, Horia Mania, Stephen Tu, Michael I. Jordan, Benjamin Recht |
| 2018 | COLT | Averaging Stochastic Gradient Descent on Riemannian Manifolds. | Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan |
| 2018 | CVPR | Partial Transfer Learning With Selective Adversarial Networks. | Zhangjie Cao, Mingsheng Long, Jianmin Wang, Michael I. Jordan |
| 2018 | ICML | On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. | Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan |
| 2018 | ICML | Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. | Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan |
| 2018 | ICML | RLlib: Abstractions for Distributed Reinforcement Learning. | Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael I. Jordan, Ion Stoica |
| 2018 | ICML | SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate. | Aaditya Ramdas, Tijana Zrnic, Martin J. Wainwright, Michael I. Jordan |
| 2018 | OSDI | Ray: A Distributed Framework for Emerging AI Applications. | Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, Ion Stoica |
| 2017 | AISTATS | Less than a Single Pass: Stochastically Controlled Stochastic Gradient. | Lihua Lei, Michael I. Jordan |
| 2017 | AISTATS | On the Learnability of Fully-Connected Neural Networks. | Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael I. Jordan |
| 2017 | HotOS | Real-Time Machine Learning: The Missing Pieces. | Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael I. Jordan, Ion Stoica |
| 2017 | ICML | How to Escape Saddle Points Efficiently. | Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan |
| 2017 | ICML | Deep Transfer Learning with Joint Adaptation Networks. | Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan |
| 2017 | ICML | Breaking Locality Accelerates Block Gauss-Seidel. | Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht |
| 2017 | ISIT | Decoding from pooled data: Phase transitions of message passing. | Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborov, Michael I. Jordan |
| 2017 | SIGMETRICS | On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic. | Michael I. Jordan |
| 2016 | AAAI | The Constrained Laplacian Rank Algorithm for Graph-Based Clustering. | Feiping Nie, Xiaoqian Wang, Michael I. Jordan, Heng Huang |
| 2016 | AISTATS | A Linearly-Convergent Stochastic L-BFGS Algorithm. | Philipp Moritz, Robert Nishihara, Michael I. Jordan |
| 2016 | COLT | Gradient Descent Only Converges to Minimizers. | Jason D. Lee, Max Simchowitz, Michael I. Jordan, Benjamin Recht |
| 2016 | ICML | A Kernelized Stein Discrepancy for Goodness-of-fit Tests. | Qiang Liu, Jason D. Lee, Michael I. Jordan |
| 2016 | ICML | L1-regularized Neural Networks are Improperly Learnable in Polynomial Time. | Yuchen Zhang, Jason D. Lee, Michael I. Jordan |
| 2016 | SPAA | On Computational Thinking, Inferential Thinking and Data Science. | Michael I. Jordan |
| 2015 | CIDR | The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. | Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan |
| 2015 | CLOUD | Automating model search for large scale machine learning. | Evan Randall Sparks, Ameet Talwalkar, Daniel Haas, Michael J. Franklin, Michael I. Jordan, Tim Kraska |
| 2015 | ICML | Learning Transferable Features with Deep Adaptation Networks. | Mingsheng Long, Yue Cao, Jianmin Wang, Michael I. Jordan |
| 2015 | ICML | Adding vs. Averaging in Distributed Primal-Dual Optimization. | Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtrik, Martin Takc |
| 2015 | ICML | A General Analysis of the Convergence of ADMM. | Robert Nishihara, Laurent Lessard, Benjamin Recht, Andrew K. Packard, Michael I. Jordan |
| 2015 | ICML | Trust Region Policy Optimization. | John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, Philipp Moritz |
| 2015 | ICML | Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds. | Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan |
| 2015 | ICRA | Optimism-driven exploration for nonlinear systems. | Teodor Mihai Moldovan, Sergey Levine, Michael I. Jordan, Pieter Abbeel |
| 2015 | PODS | Computational Thinking, Inferential Thinking and "Big Data". | Michael I. Jordan |
| 2015 | SIGMOD | Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype? | Christopher R, Divy Agrawal, Magdalena Balazinska, Michael J. Cafarella, Michael I. Jordan, Tim Kraska, Raghu Ramakrishnan |
| 2014 | COLT | Lower bounds on the performance of polynomial-time algorithms for sparse linear regression. | Yuchen Zhang, Martin J. Wainwright, Michael I. Jordan |
| 2014 | RECOMB | Changepoint Analysis for Efficient Variant Calling. | Adam E. Bloniarz, Ameet Talwalkar, Jonathan Terhorst, Michael I. Jordan, David A. Patterson, Bin Yu, Yun S. Song |
| 2014 | SIGMOD | Knowing when you're wrong: building fast and reliable approximate query processing systems. | Sameer Agarwal, Henry Milner, Ariel Kleiner, Ameet Talwalkar, Michael I. Jordan, Samuel Madden, Barzan Mozafari, Ion Stoica |
| 2013 | CIDR | MLbase: A Distributed Machine-learning System. | Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan |
| 2013 | FOCS | Local Privacy and Statistical Minimax Rates. | John C. Duchi, Michael I. Jordan, Martin J. Wainwright |
| 2013 | ICCV | Distributed Low-Rank Subspace Segmentation. | Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan |
| 2013 | ICDM | MLI: An API for Distributed Machine Learning. | Evan Randall Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska |
| 2013 | ICML | MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. | Tamara Broderick, Brian Kulis, Michael I. Jordan |
| 2013 | ICML | Efficient Ranking from Pairwise Comparisons. | Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic |
| 2013 | KDD | A general bootstrap performance diagnostic. | Ariel Kleiner, Ameet Talwalkar, Sameer Agarwal, Ion Stoica, Michael I. Jordan |
| 2012 | ICML | The Big Data Bootstrap. | Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan |
| 2012 | ICML | Revisiting k-means: New Algorithms via Bayesian Nonparametrics. | Brian Kulis, Michael I. Jordan |
| 2012 | ICML | Variational Bayesian Inference with Stochastic Search. | John W. Paisley, David M. Blei, Michael I. Jordan |
| 2012 | ICML | Nonparametric Link Prediction in Dynamic Networks. | Purnamrita Sarkar, Deepayan Chakrabarti, Michael I. Jordan |
| 2012 | KDD | Divide-and-conquer and statistical inference for big data. | Michael I. Jordan |
| 2012 | KDD | Active spectral clustering via iterative uncertainty reduction. | Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan |
| 2011 | ACL | Learning Dependency-Based Compositional Semantics. | Percy Liang, Michael I. Jordan, Dan Klein |
| 2011 | CVPR | Supervised hierarchical Pitman-Yor process for natural scene segmentation. | Alex Shyr, Trevor Darrell, Michael I. Jordan, Raquel Urtasun |
| 2011 | FAST | The SCADS Director: Scaling a Distributed Storage System Under Stringent Performance Requirements. | Beth Trushkowsky, Peter Bodk, Armando Fox, Michael J. Franklin, Michael I. Jordan, David A. Patterson |
| 2011 | ICML | A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection. | Yue Guan, Jennifer G. Dy, Michael I. Jordan |
| 2011 | RECOMB | Nonparametric Combinatorial Sequence Models. | Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic |
| 2011 | SIGCOMM | Managing data transfers in computer clusters with orchestra. | Mosharaf Chowdhury, Matei Zaharia, Justin Ma, Michael I. Jordan, Ion Stoica |
| 2011 | SDM | Nonparametric Bayesian Co-clustering Ensembles. | Pu Wang, Kathryn B. Laskey, Carlotta Domeniconi, Michael I. Jordan |
| 2010 | CLOUD | Characterizing, modeling, and generating workload spikes for stateful services. | Peter Bodk, Armando Fox, Michael J. Franklin, Michael I. Jordan, David A. Patterson |
| 2010 | CVPR | Sufficient dimension reduction for visual sequence classification. | Alex Shyr, Raquel Urtasun, Michael I. Jordan |
| 2010 | ICML | On the Consistency of Ranking Algorithms. | John C. Duchi, Lester W. Mackey, Michael I. Jordan |
| 2010 | ICML | Learning Programs: A Hierarchical Bayesian Approach. | Percy Liang, Michael I. Jordan, Dan Klein |
| 2010 | ICML | Mixed Membership Matrix Factorization. | Lester W. Mackey, David J. Weiss, Michael I. Jordan |
| 2010 | ICML | Multiple Non-Redundant Spectral Clustering Views. | Donglin Niu, Jennifer G. Dy, Michael I. Jordan |
| 2010 | ICML | An Analysis of the Convergence of Graph Laplacians. | Daniel Ting, Ling Huang, Michael I. Jordan |
| 2010 | ICML | Detecting Large-Scale System Problems by Mining Console Logs. | Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan |
| 2010 | NAACL | Type-Based MCMC. | Percy Liang, Michael I. Jordan, Dan Klein |
| 2010 | OSDI | Experience Mining Google's Production Console Logs. | Wei Xu, Ling Huang, Michael I. Jordan |
| 2010 | UAI | Modeling Events with Cascades of Poisson Processes. | Aleksandr Simma, Michael I. Jordan |
| 2009 | ACL | Learning Semantic Correspondences with Less Supervision. | Percy Liang, Michael I. Jordan, Dan Klein |
| 2009 | ICDE | Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning. | Archana Ganapathi, Harumi A. Kuno, Umeshwar Dayal, Janet L. Wiener, Armando Fox, Michael I. Jordan, David A. Patterson |
| 2009 | ICDM | Online System Problem Detection by Mining Patterns of Console Logs. | Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan |
| 2009 | ICML | Learning from measurements in exponential families. | Percy Liang, Michael I. Jordan, Dan Klein |
| 2009 | KDD | Fast approximate spectral clustering. | Donghui Yan, Ling Huang, Michael I. Jordan |
| 2009 | SODA | Combinatorial stochastic processes and nonparametric Bayesian modeling. | Michael I. Jordan |
| 2009 | UAI | Optimization of Structured Mean Field Objectives. | Alexandre Bouchard-Ct, Michael I. Jordan |
| 2009 | SOSP | Detecting large-scale system problems by mining console logs. | Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan |
| 2008 | ICDM | Nonnegative Matrix Factorization for Combinatorial Optimization: Spectral Clustering, Graph Matching, and Clique Finding. | Chris H. Q. Ding, Tao Li, Michael I. Jordan |
| 2008 | ICML | An HDP-HMM for systems with state persistence. | Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky |
| 2008 | ICML | An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. | Percy Liang, Michael I. Jordan |
| 2008 | OSDI | Probabilistic Inference in Queueing Networks. | Charles Sutton, Michael I. Jordan |
| 2008 | OSDI | Mining Console Logs for Large-Scale System Problem Detection. | Wei Xu, Ling Huang, Armando Fox, David A. Patterson, Michael I. Jordan |
| 2008 | RECOMB | On the Inference of Ancestries in Admixed Populations. | Sriram Sankararaman, Gad Kimmel, Eran Halperin, Michael I. Jordan |
| 2008 | UAI | The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. | Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan |
| 2007 | EMNLP | The Infinite PCFG Using Hierarchical Dirichlet Processes. | Percy Liang, Slav Petrov, Michael I. Jordan, Dan Klein |
| 2007 | ICCV | Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. | Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan |
| 2007 | ICDM | Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization. | Tao Li, Chris H. Q. Ding, Michael I. Jordan |
| 2007 | ICIP | Image Denoising with Nonparametric Hidden Markov Trees. | Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan |
| 2007 | ICML | A permutation-augmented sampler for DP mixture models. | Percy Liang, Michael I. Jordan, Benjamin Taskar |
| 2007 | ICML | Regression on manifolds using kernel dimension reduction. | Jens Nilsson, Fei Sha, Michael I. Jordan |
| 2007 | INFOCOM | Communication-Efficient Online Detection of Network-Wide Anomalies. | Ling Huang, XuanLong Nguyen, Minos N. Garofalakis, Joseph M. Hellerstein, Michael I. Jordan, Anthony D. Joseph, Nina Taft |
| 2007 | ISIT | Nonparametric estimation of the likelihood ratio and divergence functionals. | XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan |
| 2006 | ICML | A graphical model for predicting protein molecular function. | Barbara E. Engelhardt, Michael I. Jordan, Steven E. Brenner |
| 2006 | ICML | Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture. | Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh |
| 2006 | ICML | Statistical debugging: simultaneous identification of multiple bugs. | Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken |
| 2006 | ISIT | On optimal quantization rules for sequential decision problems. | XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan |
| 2006 | NAACL | Word Alignment via Quadratic Assignment. | Simon Lacoste-Julien, Benjamin Taskar, Dan Klein, Michael I. Jordan |
| 2006 | UAI | Bayesian Multicategory Support Vector Machines. | Zhihua Zhang, Michael I. Jordan |
| 2005 | AISTATS | Semiparametric latent factor models. | Yee Whye Teh, Matthias W. Seeger, Michael I. Jordan |
| 2005 | ICASSP | Discriminative training of hidden Markov models for multiple pitch tracking [speech processing examples]. | Francis R. Bach, Michael I. Jordan |
| 2005 | ICASSP | Multi-instrument musical transcription using a dynamic graphical model. | Brian K. Vogel, Michael I. Jordan, David Wessel |
| 2005 | ICML | Predictive low-rank decomposition for kernel methods. | Francis R. Bach, Michael I. Jordan |
| 2005 | PLDI | Scalable statistical bug isolation. | Ben Liblit, Mayur Naik, Alice X. Zheng, Alex Aiken, Michael I. Jordan |
| 2005 | UAI | The DLR Hierarchy of Approximate Inference. | Michal Rosen-Zvi, Michael I. Jordan, Alan L. Yuille |
| 2004 | ICML | Multiple kernel learning, conic duality, and the SMO algorithm. | Francis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan |
| 2004 | ICML | Variational methods for the Dirichlet process. | David M. Blei, Michael I. Jordan |
| 2004 | ICML | Decentralized detection and classification using kernel methods. | XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan |
| 2004 | ICML | Bayesian haplo-type inference via the dirichlet process. | Eric P. Xing, Roded Sharan, Michael I. Jordan |
| 2004 | PSB | Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast. | Gert R. G. Lanckriet, Minghua Deng, Nello Cristianini, Michael I. Jordan, William Stafford Noble |
| 2004 | UAI | Graph Partition Strategies for Generalized Mean Field Inference. | Eric P. Xing, Michael I. Jordan |
| 2003 | DAC | Support vector machines for analog circuit performance representation. | Fernando De Bernardinis, Michael I. Jordan, Alberto L. Sangiovanni-Vincentelli |
| 2003 | ICASSP | Kernel independent component analysis. | Francis R. Bach, Michael I. Jordan |
| 2003 | PLDI | Bug isolation via remote program sampling. | Ben Liblit, Alex Aiken, Alice X. Zheng, Michael I. Jordan |
| 2003 | SIGIR | Modeling annotated data. | David M. Blei, Michael I. Jordan |
| 2003 | UAI | A generalized mean field algorithm for variational inference in exponential families. | Eric P. Xing, Michael I. Jordan, Stuart Russell |
| 2002 | ICML | Learning the Kernel Matrix with Semi-Definite Programming. | Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan |
| 2002 | UAI | Tree-dependent Component Analysis. | Francis R. Bach, Michael I. Jordan |
| 2002 | UAI | Loopy Belief Propogation and Gibbs Measures. | Sekhar Tatikonda, Michael I. Jordan |
| 2002 | WABI | Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces. | L. R. Grate, Chiranjib Bhattacharyya, Michael I. Jordan, I. Saira Mian |
| 2001 | ICML | Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. | Andrew Y. Ng, Michael I. Jordan |
| 2001 | ICML | Feature selection for high-dimensional genomic microarray data. | Eric P. Xing, Michael I. Jordan, Richard M. Karp |
| 2001 | IJCAI | Link Analysis, Eigenvectors and Stability. | Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan |
| 2001 | SIGIR | Stable Algorithms for Link Analysis. | Alice X. Zheng, Andrew Y. Ng, Michael I. Jordan |
| 2001 | UAI | Efficient Stepwise Selection in Decomposable Models. | Amol Deshpande, Minos N. Garofalakis, Michael I. Jordan |
| 2000 | UAI | PEGASUS: A policy search method for large MDPs and POMDPs. | Andrew Y. Ng, Michael I. Jordan |
| 1999 | UAI | Loopy Belief Propagation for Approximate Inference: An Empirical Study. | Kevin P. Murphy, Yair Weiss, Michael I. Jordan |
| 1998 | UAI | Mixture Representations for Inference and Learning in Boltzmann Machines. | Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan |
| 1997 | AISTATS | A Variational Approach to Bayesian Logistic Regression Models and their Extensions. | Tommi S. Jaakkola, Michael I. Jordan |
| 1997 | AISTATS | An Objective Function for Belief Net Triangulation. | Marina Meila, Michael I. Jordan |
| 1997 | AISTATS | Mixed Memory Markov Models. | Lawrence K. Saul, Michael I. Jordan |
| 1996 | UAI | Computing upper and lower bounds on likelihoods in intractable networks. | Tommi S. Jaakkola, Michael I. Jordan |
| 1994 | COLT | A Statistical Approach to Decision Tree Modeling. | Michael I. Jordan |
| 1994 | ICML | A Statistical Approach to Decision Tree Modeling. | Michael I. Jordan |
| 1994 | ICML | Learning Without State-Estimation in Partially Observable Markovian Decision Processes. | Satinder P. Singh, Tommi S. Jaakkola, Michael I. Jordan |
| 1993 | ICML | Supervised Learning and Divide-and-Conquer: A Statistical Approach. | Michael I. Jordan, Robert A. Jacobs |
| 1991 | ICML | Internal World Models and Supervised Learning. | Michael I. Jordan, David E. Rumelhart |
| 1990 | IJCNN | A | Pietro Mazzoni, Richard A. Andersen, Michael I. Jordan |