Learning to Control an Unstable System with Forward Modeling. - Michael I. Jordan; Robert A. Jacobs (NIPS, 1989)

A R-P learning applied to a network model of cortical area 7a. - Pietro Mazzoni; Richard A. Andersen; Michael I. Jordan (IJCNN, 1990)

A Competitive Modular Connectionist Architecture. - Robert A. Jacobs; Michael I. Jordan (NIPS, 1990)

Internal World Models and Supervised Learning. - Michael I. Jordan; David E. Rumelhart (ML, 1991)

Forward Dynamics Modeling of Speech Motor Control Using Physiological Data. - Makoto Hirayama; Eric Vatikiotis-Bateson; Mitsuo Kawato; Michael I. Jordan (NIPS, 1991)

Hierarchies of Adaptive Experts. - Michael I. Jordan; Robert A. Jacobs (NIPS, 1991)

Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. - Robert A. Jacobs; Michael I. Jordan; Andrew G. Barto (Cogn. Sci., 1991)

Adaptive Mixtures of Local Experts. - Robert A. Jacobs; Michael I. Jordan; Steven J. Nowlan; Geoffrey E. Hinton (Neural Comput., 1991)

A Dynamical Model of Priming and Repetition Blindness. - Daphne Bavelier; Michael I. Jordan (NIPS, 1992)

Forward Models: Supervised Learning with a Distal Teacher. - Michael I. Jordan; David E. Rumelhart (Cogn. Sci., 1992)

Supervised Learning and Divide-and-Conquer: A Statistical Approach. - Michael I. Jordan; Robert A. Jacobs (ICML, 1993)

Supervised learning from incomplete data via an EM approach. - Zoubin Ghahramani; Michael I. Jordan (NIPS, 1993)

Task Decompostiion Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. - Robert A. Jacobs; Michael I. Jordan; Andrew G. Barto (Machine Learning: From Theory to Applications, 1993)

Learning piecewise control strategies in a modular neural network architecture. - Robert A. Jacobs; Michael I. Jordan (IEEE Trans. Syst. Man Cybern., 1993)

A Statistical Approach to Decision Tree Modeling. - Michael I. Jordan (ICML, 1994)

Learning Without State-Estimation in Partially Observable Markovian Decision Processes. - Satinder P. Singh; Tommi S. Jaakkola; Michael I. Jordan (ICML, 1994)

Computational Structure of coordinate transformations: A generalization study. - Zoubin Ghahramani; Daniel M. Wolpert; Michael I. Jordan (NIPS, 1994)

Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. - Tommi S. Jaakkola; Satinder Singh 001; Michael I. Jordan (NIPS, 1994)

Boltzmann Chains and Hidden Markov Models. - Lawrence K. Saul; Michael I. Jordan (NIPS, 1994)

Reinforcement Learning with Soft State Aggregation. - Satinder Singh 001; Tommi S. Jaakkola; Michael I. Jordan (NIPS, 1994)

Forward dynamic models in human motor control: Psychophysical evidence. - Daniel M. Wolpert; Zoubin Ghahramani; Michael I. Jordan (NIPS, 1994)

An Alternative Model for Mixtures of Experts. - Lei Xu 001; Michael I. Jordan; Geoffrey E. Hinton (NIPS, 1994)

On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. - Tommi S. Jaakkola; Michael I. Jordan; Satinder P. Singh (Neural Comput., 1994)

Hierarchical Mixtures of Experts and the EM Algorithm. - Michael I. Jordan; Robert A. Jacobs (Neural Comput., 1994)

Learning in Boltzmann Trees. - Lawrence K. Saul; Michael I. Jordan (Neural Comput., 1994)

Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. - Tommi S. Jaakkola; Lawrence K. Saul; Michael I. Jordan (NIPS, 1995)

Learning Fine Motion by Markov Mixtures of Experts. - Marina Meila; Michael I. Jordan (NIPS, 1995)

Reinforcement Learning by Probability Matching. - Philip N. Sabes; Michael I. Jordan (NIPS, 1995)

Exploiting Tractable Substructures in Intractable Networks. - Lawrence K. Saul; Michael I. Jordan (NIPS, 1995)

Convergence results for the EM approach to mixtures of experts architectures. - Michael I. Jordan; Lei Xu 001 (Neural Networks, 1995)

Recursive Algorithms for Approximating Probabilities in Graphical Models. - Tommi S. Jaakkola; Michael I. Jordan (NIPS, 1996)

Hidden Markov Decision Trees. - Michael I. Jordan; Zoubin Ghahramani; Lawrence K. Saul (NIPS, 1996)

Triangulation by Continuous Embedding. - Marina Meila; Michael I. Jordan (NIPS, 1996)

A Variational Principle for Model-based Morphing. - Lawrence K. Saul; Michael I. Jordan (NIPS, 1996)

Computing upper and lower bounds on likelihoods in intractable networks. - Tommi S. Jaakkola; Michael I. Jordan (UAI, 1996)

Neural Networks. - Michael I. Jordan; Christopher M. Bishop (ACM Comput. Surv., 1996)

Active Learning with Statistical Models. - David A. Cohn; Zoubin Ghahramani; Michael I. Jordan (J. Artif. Intell. Res., 1996)

Mean Field Theory for Sigmoid Belief Networks. - Lawrence K. Saul; Tommi S. Jaakkola; Michael I. Jordan (J. Artif. Intell. Res., 1996)

On Convergence Properties of the EM Algorithm for Gaussian Mixtures. - Lei Xu 001; Michael I. Jordan (Neural Comput., 1996)

Local linear perceptrons for classification. - Ethem Alpaydin; Michael I. Jordan (IEEE Trans. Neural Networks, 1996)

Neural Networks. - Michael I. Jordan; Christopher M. Bishop (The Computer Science and Engineering Handbook, 1997)

A Variational Approach to Bayesian Logistic Regression Models and their Extensions. - Tommi S. Jaakkola; Michael I. Jordan (AISTATS, 1997)

An Objective Function for Belief Net Triangulation. - Marina Meila; Michael I. Jordan (AISTATS, 1997)

Mixed Memory Markov Models. - Lawrence K. Saul; Michael I. Jordan (AISTATS, 1997)

Approximating Posterior Distributions in Belief Networks Using Mixtures. - Christopher M. Bishop; Neil D. Lawrence; Tommi S. Jaakkola; Michael I. Jordan (NIPS, 1997)

Adaptation in Speech Motor Control. - John F. Houde; Michael I. Jordan (NIPS, 1997)

Estimating Dependency Structure as a Hidden Variable. - Marina Meila; Michael I. Jordan (NIPS, 1997)

Factorial Hidden Markov Models. - Zoubin Ghahramani; Michael I. Jordan (Mach. Learn., 1997)

Probabilistic Independence Networks for Hidden Markov Probability Models. - Padhraic Smyth; David Heckerman; Michael I. Jordan (Neural Comput., 1997)

Improving the Mean Field Approximation Via the Use of Mixture Distributions. - Tommi S. Jaakkola; Michael I. Jordan (Learning in Graphical Models, 1998)

An Introduction to Variational Methods for Graphical Models. - Michael I. Jordan; Zoubin Ghahramani; Tommi S. Jaakkola; Lawrence K. Saul (Learning in Graphical Models, 1998)

A Mean Field Learning Algorithm for Unsupervised Neural Networks. - Lawrence K. Saul; Michael I. Jordan (Learning in Graphical Models, 1998)

Learning from Dyadic Data. - Thomas Hofmann 001; Jan Puzicha; Michael I. Jordan (NIPS, 1998)

Mixture Representations for Inference and Learning in Boltzmann Machines. - Neil D. Lawrence; Christopher M. Bishop; Michael I. Jordan (UAI, 1998)

Approximate Inference A lgorithms for Two-Layer Bayesian Networks. - Andrew Y. Ng; Michael I. Jordan (NIPS, 1999)

Loopy Belief Propagation for Approximate Inference: An Empirical Study. - Kevin P. Murphy; Yair Weiss; Michael I. Jordan (UAI, 1999)

Variational Probabilistic Inference and the QMR-DT Network. - Tommi S. Jaakkola; Michael I. Jordan (J. Artif. Intell. Res., 1999)

An Introduction to Variational Methods for Graphical Models. - Michael I. Jordan; Zoubin Ghahramani; Tommi S. Jaakkola; Lawrence K. Saul (Mach. Learn., 1999)

Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. - Lawrence K. Saul; Michael I. Jordan (Mach. Learn., 1999)

PEGASUS: A policy search method for large MDPs and POMDPs. - Andrew Y. Ng; Michael I. Jordan (UAI, 2000)

Learning with Mixtures of Trees. - Marina Meila; Michael I. Jordan (J. Mach. Learn. Res., 2000)

Attractor Dynamics in Feedforward Neural Networks. - Lawrence K. Saul; Michael I. Jordan (Neural Comput., 2000)

Bayesian parameter estimation via variational methods. - Tommi S. Jaakkola; Michael I. Jordan (Stat. Comput., 2000)

Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. - Andrew Y. Ng; Michael I. Jordan (ICML, 2001)

Feature selection for high-dimensional genomic microarray data. - Eric P. Xing; Michael I. Jordan; Richard M. Karp (ICML, 2001)

Link Analysis, Eigenvectors and Stability. - Andrew Y. Ng; Alice X. Zheng; Michael I. Jordan (IJCAI, 2001)

Thin Junction Trees. - Francis R. Bach; Michael I. Jordan (NIPS, 2001)

Minimax Probability Machine. - Gert R. G. Lanckriet; Laurent El Ghaoui; Chiranjib Bhattacharyya; Michael I. Jordan (NIPS, 2001)

On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. - Andrew Y. Ng; Michael I. Jordan (NIPS, 2001)

On Spectral Clustering: Analysis and an algorithm. - Andrew Y. Ng; Michael I. Jordan; Yair Weiss (NIPS, 2001)

Stable Algorithms for Link Analysis. - Alice X. Zheng; Andrew Y. Ng; Michael I. Jordan (SIGIR, 2001)

Efficient Stepwise Selection in Decomposable Models. - Amol Deshpande; Minos N. Garofalakis; Michael I. Jordan (UAI, 2001)

Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures. - Jinwen Ma; Lei Xu 001; Michael I. Jordan (Neural Comput., 2001)

Learning the Kernel Matrix with Semi-Definite Programming. - Gert R. G. Lanckriet; Nello Cristianini; Peter L. Bartlett; Laurent El Ghaoui; Michael I. Jordan (ICML, 2002)

Learning Graphical Models with Mercer Kernels. - Francis R. Bach; Michael I. Jordan (NIPS, 2002)

Robust Novelty Detection with Single-Class MPM. - Gert R. G. Lanckriet; Laurent El Ghaoui; Michael I. Jordan (NIPS, 2002)

A Minimal Intervention Principle for Coordinated Movement. - Emanuel Todorov; Michael I. Jordan (NIPS, 2002)

A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences. - Eric P. Xing; Michael I. Jordan; Richard M. Karp; Stuart Russell 001 (NIPS, 2002)

Distance Metric Learning with Application to Clustering with Side-Information. - Eric P. Xing; Andrew Y. Ng; Michael I. Jordan; Stuart Russell 001 (NIPS, 2002)

Tree-dependent Component Analysis. - Francis R. Bach; Michael I. Jordan (UAI, 2002)

Loopy Belief Propogation and Gibbs Measures. - Sekhar Tatikonda; Michael I. Jordan (UAI, 2002)

Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces. - L. R. Grate; Chiranjib Bhattacharyya; Michael I. Jordan; I. Saira Mian (WABI, 2002)

Kernel Independent Component Analysis. - Francis R. Bach; Michael I. Jordan (J. Mach. Learn. Res., 2002)

A Robust Minimax Approach to Classification. - Gert R. G. Lanckriet; Laurent El Ghaoui; Chiranjib Bhattacharyya; Michael I. Jordan (J. Mach. Learn. Res., 2002)

Graphical Models: Foundations of Neural Computation. - Michael I. Jordan; Terrence J. Sejnowski (Pattern Anal. Appl., 2002)

LOGOS: a modular Bayesian model for de novo motif detection. - Eric P. Xing; Wei Wu 023; Michael I. Jordan; Richard M. Karp (CSB, 2003)

Support vector machines for analog circuit performance representation. - Fernando De Bernardinis; Michael I. Jordan; Alberto L. Sangiovanni-Vincentelli (DAC, 2003)

Learning Spectral Clustering. - Francis R. Bach; Michael I. Jordan (NIPS, 2003)

Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. - Peter L. Bartlett; Michael I. Jordan; Jon D. McAuliffe (NIPS, 2003)

Hierarchical Topic Models and the Nested Chinese Restaurant Process. - David M. Blei; Thomas L. Griffiths 001; Michael I. Jordan; Joshua B. Tenenbaum (NIPS, 2003)

Kernel Dimensionality Reduction for Supervised Learning. - Kenji Fukumizu; Francis R. Bach; Michael I. Jordan (NIPS, 2003)

Autonomous Helicopter Flight via Reinforcement Learning. - Andrew Y. Ng; H. Jin Kim; Michael I. Jordan; Shankar Sastry 001 (NIPS, 2003)

On the Concentration of Expectation and Approximate Inference in Layered Networks. - XuanLong Nguyen; Michael I. Jordan (NIPS, 2003)

Semidefinite Relaxations for Approximate Inference on Graphs with Cycles. - Martin J. Wainwright; Michael I. Jordan (NIPS, 2003)

Statistical Debugging of Sampled Programs. - Alice X. Zheng; Michael I. Jordan; Ben Liblit; Alex Aiken (NIPS, 2003)

Bug isolation via remote program sampling. - Ben Liblit; Alex Aiken; Alice X. Zheng; Michael I. Jordan (PLDI, 2003)

Modeling annotated data. - David M. Blei; Michael I. Jordan (SIGIR, 2003)

A generalized mean field algorithm for variational inference in exponential families. - Eric P. Xing; Michael I. Jordan; Stuart Russell 001 (UAI, 2003)

Beyond Independent Components: Trees and Clusters. - Francis R. Bach; Michael I. Jordan (J. Mach. Learn. Res., 2003)

Matching Words and Pictures. - Kobus Barnard; Pinar Duygulu; David A. Forsyth; Nando de Freitas; David M. Blei; Michael I. Jordan (J. Mach. Learn. Res., 2003)

Latent Dirichlet Allocation. - David M. Blei; Andrew Y. Ng; Michael I. Jordan (J. Mach. Learn. Res., 2003)

An Introduction to MCMC for Machine Learning. - Christophe Andrieu; Nando de Freitas; Arnaud Doucet; Michael I. Jordan (Mach. Learn., 2003)

Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data. - Chiranjib Bhattacharyya; L. R. Grate; A. Rizki; D. Radisky; F. J. Molina; Michael I. Jordan; Mina J. Bissell; I. Saira Mian (Signal Process., 2003)

Extensions of the Informative Vector Machine. - Neil D. Lawrence; John C. Platt; Michael I. Jordan (Deterministic and Statistical Methods in Machine Learning, 2004)

Failure Diagnosis Using Decision Trees. - Mike Y. Chen; Alice X. Zheng; Jim Lloyd; Michael I. Jordan; Eric A. Brewer (ICAC, 2004)

Multiple kernel learning, conic duality, and the SMO algorithm. - Francis R. Bach; Gert R. G. Lanckriet; Michael I. Jordan (ICML, 2004)

Variational methods for the Dirichlet process. - David M. Blei; Michael I. Jordan (ICML, 2004)

Decentralized detection and classification using kernel methods. - XuanLong Nguyen; Martin J. Wainwright; Michael I. Jordan (ICML, 2004)

Bayesian haplo-type inference via the dirichlet process. - Eric P. Xing; Roded Sharan; Michael I. Jordan (ICML, 2004)

Blind One-microphone Speech Separation: A Spectral Learning Approach. - Francis R. Bach; Michael I. Jordan (NIPS, 2004)

Computing regularization paths for learning multiple kernels. - Francis R. Bach; Romain Thibaux; Michael I. Jordan (NIPS, 2004)

Semi-supervised Learning via Gaussian Processes. - Neil D. Lawrence; Michael I. Jordan (NIPS, 2004)

Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. - Yee Whye Teh; Michael I. Jordan; Matthew J. Beal; David M. Blei (NIPS, 2004)

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 (Pacific Symposium on Biocomputing, 2004)

Graph Partition Strategies for Generalized Mean Field Inference. - Eric P. Xing; Michael I. Jordan (UAI, 2004)

A statistical framework for genomic data fusion. - Gert R. G. Lanckriet; Tijl De Bie; Nello Cristianini; Michael I. Jordan; William Stafford Noble (Bioinform., 2004)

Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. - Jon D. McAuliffe; Lior Pachter; Michael I. Jordan (Bioinform., 2004)

Logos: a Modular Bayesian Model for de Novo Motif Detection. - Eric P. Xing; Wei Wu 023; Michael I. Jordan; Richard M. Karp (J. Bioinform. Comput. Biol., 2004)

Robust Sparse Hyperplane Classifiers: Application to Uncertain Molecular Profiling Data. - Chiranjib Bhattacharyya; L. R. Grate; Michael I. Jordan; Laurent El Ghaoui; I. Saira Mian (J. Comput. Biol., 2004)

Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces. - Kenji Fukumizu; Francis R. Bach; Michael I. Jordan (J. Mach. Learn. Res., 2004)

Learning the Kernel Matrix with Semidefinite Programming. - Gert R. G. Lanckriet; Nello Cristianini; Peter L. Bartlett; Laurent El Ghaoui; Michael I. Jordan (J. Mach. Learn. Res., 2004)

Kalman filtering with intermittent observations. - Bruno Sinopoli; Luca Schenato 001; Massimo Franceschetti; Kameshwar Poolla; Michael I. Jordan; Shankar S. Sastry (IEEE Trans. Autom. Control., 2004)

Learning graphical models for stationary time series. - Francis R. Bach; Michael I. Jordan (IEEE Trans. Signal Process., 2004)

Semiparametric latent factor models. - Yee Whye Teh; Matthias W. Seeger; Michael I. Jordan (AISTATS, 2005)

Modèles de Markov cachés pour l'estimation de plusieurs fréquences fondamentales. - Michael I. Jordan; Francis R. Bach (EGC (Ateliers), 2005)

Combining Visualization and Statistical Analysis to Improve Operator Confidence and Efficiency for Failure Detection and Localization. - Peter Bodík; Greg Friedman; Lukas Biewald; Helen Levine; George Candea; Kayur Patel; Gilman Tolle; Jonathan Hui; Armando Fox; Michael I. Jordan; David A. Patterson 001 (ICAC, 2005)

Discriminative training of hidden Markov models for multiple pitch tracking [speech processing examples]. - Francis R. Bach; Michael I. Jordan (ICASSP (5), 2005)

Multi-instrument musical transcription using a dynamic graphical model. - Brian K. Vogel; Michael I. Jordan; David Wessel (ICASSP (5), 2005)

Predictive low-rank decomposition for kernel methods. - Francis R. Bach; Michael I. Jordan (ICML, 2005)

Robust design of biological experiments. - Patrick Flaherty; Michael I. Jordan; Adam P. Arkin (NIPS, 2005)

Divergences, surrogate loss functions and experimental design. - XuanLong Nguyen; Martin J. Wainwright; Michael I. Jordan (NIPS, 2005)

Structured Prediction via the Extragradient Method. - Benjamin Taskar; Simon Lacoste-Julien; Michael I. Jordan (NIPS, 2005)

Scalable statistical bug isolation. - Ben Liblit; Mayur Naik; Alice X. Zheng; Alex Aiken; Michael I. Jordan (PLDI, 2005)

The DLR Hierarchy of Approximate Inference. - Michal Rosen-Zvi; Michael I. Jordan; Alan L. Yuille (UAI, 2005)

A latent variable model for chemogenomic profiling. - Patrick Flaherty; Guri Giaever; Jochen Kumm; Michael I. Jordan; Adam P. Arkin (Bioinform., 2005)

On divergences, surrogate loss functions, and decentralized detection - XuanLong Nguyen; Martin J. Wainwright; Michael I. Jordan (CoRR, 2005)

Protein Molecular Function Prediction by Bayesian Phylogenomics. - Barbara E. Engelhardt; Michael I. Jordan; Kathryn E. Muratore; Steven E. Brenner (PLoS Comput. Biol., 2005)

A kernel-based learning approach to ad hoc sensor network localization. - XuanLong Nguyen; Michael I. Jordan; Bruno Sinopoli (ACM Trans. Sens. Networks, 2005)

Nonparametric decentralized detection using kernel methods. - XuanLong Nguyen; Martin J. Wainwright; Michael I. Jordan (IEEE Trans. Signal Process., 2005)

Gaussian Processes and the Null-Category Noise Model. - Neil D. Lawrence; Michael I. Jordan (Semi-Supervised Learning, 2006)

A graphical model for predicting protein molecular function. - Barbara E. Engelhardt; Michael I. Jordan; Steven E. Brenner (ICML, 2006)

Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture. - Eric P. Xing; Kyung-Ah Sohn; Michael I. Jordan; Yee Whye Teh (ICML, 2006)

Statistical debugging: simultaneous identification of multiple bugs. - Alice X. Zheng; Michael I. Jordan; Ben Liblit; Mayur Naik; Alex Aiken (ICML, 2006)

On optimal quantization rules for sequential decision problems. - XuanLong Nguyen; Martin J. Wainwright; Michael I. Jordan (ISIT, 2006)

Word Alignment via Quadratic Assignment. - Simon Lacoste-Julien; Benjamin Taskar; Dan Klein 001; Michael I. Jordan (HLT-NAACL, 2006)

In-Network PCA and Anomaly Detection. - Ling Huang 001; XuanLong Nguyen; Minos N. Garofalakis; Michael I. Jordan; Anthony D. Joseph; Nina Taft (NIPS, 2006)

Bayesian Multicategory Support Vector Machines. - Zhihua Zhang 004; Michael I. Jordan (UAI, 2006)

Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span. - David M. Blei; K. Franks; Michael I. Jordan; I. Saira Mian (BMC Bioinform., 2006)

On optimal quantization rules for some sequential decision problems - XuanLong Nguyen; Martin J. Wainwright; Michael I. Jordan (CoRR, 2006)

Learning Spectral Clustering, With Application To Speech Separation. - Francis R. Bach; Michael I. Jordan (J. Mach. Learn. Res., 2006)

Structured Prediction, Dual Extragradient and Bregman Projections. - Benjamin Taskar; Simon Lacoste-Julien; Michael I. Jordan (J. Mach. Learn. Res., 2006)

Nonparametric empirical Bayes for the Dirichlet process mixture model. - Jon D. McAuliffe; David M. Blei; Michael I. Jordan (Stat. Comput., 2006)

Log-determinant relaxation for approximate inference in discrete Markov random fields. - Martin J. Wainwright; Michael I. Jordan (IEEE Trans. Signal Process., 2006)

Statistical Machine Learning and Computational Biology. - Michael I. Jordan (BIBM, 2007)

The Infinite PCFG Using Hierarchical Dirichlet Processes. - Percy Liang; Slav Petrov; Michael I. Jordan; Dan Klein 001 (EMNLP-CoNLL, 2007)

Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. - Jyri J. Kivinen; Erik B. Sudderth; Michael I. Jordan (ICCV, 2007)

Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization. - Tao Li 001; Chris H. Q. Ding; Michael I. Jordan (ICDM, 2007)

Image Denoising with Nonparametric Hidden Markov Trees. - Jyri J. Kivinen; Erik B. Sudderth; Michael I. Jordan (ICIP (3), 2007)

A permutation-augmented sampler for DP mixture models. - Percy Liang; Michael I. Jordan; Benjamin Taskar (ICML, 2007)

Regression on manifolds using kernel dimension reduction. - Jens Nilsson 002; Fei Sha; Michael I. Jordan (ICML, 2007)

Communication-Efficient Online Detection of Network-Wide Anomalies. - Ling Huang 001; XuanLong Nguyen; Minos N. Garofalakis; Joseph M. Hellerstein; Michael I. Jordan; Anthony D. Joseph; Nina Taft (INFOCOM, 2007)

Nonparametric estimation of the likelihood ratio and divergence functionals. - XuanLong Nguyen; Martin J. Wainwright; Michael I. Jordan (ISIT, 2007)

Feature Selection Methods for Improving Protein Structure Prediction with Rosetta. - Ben Blum; Michael I. Jordan; David E. Kim; Rhiju Das; Philip Bradley; David Baker 001 (NIPS, 2007)

Agreement-Based Learning. - Percy Liang; Dan Klein 001; Michael I. Jordan (NIPS, 2007)

Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization. - XuanLong Nguyen; Martin J. Wainwright; Michael I. Jordan (NIPS, 2007)

Bayesian Haplotype Inference via the Dirichlet Process. - Eric P. Xing; Michael I. Jordan; Roded Sharan (J. Comput. Biol., 2007)

Hierarchical Beta Processes and the Indian Buffet Process. - Romain Thibaux; Michael I. Jordan (AISTATS, 2007)

A Direct Formulation for Sparse PCA Using Semidefinite Programming. - Alexandre d'Aspremont; Laurent El Ghaoui; Michael I. Jordan; Gert R. G. Lanckriet (SIAM Rev., 2007)

Union support recovery in high-dimensional multivariate regression. - Guillaume Obozinski; Martin J. Wainwright; Michael I. Jordan (Allerton, 2008)

Nonnegative Matrix Factorization for Combinatorial Optimization: Spectral Clustering, Graph Matching, and Clique Finding. - Chris H. Q. Ding; Tao Li 001; Michael I. Jordan (ICDM, 2008)

An HDP-HMM for systems with state persistence. - Emily B. Fox; Erik B. Sudderth; Michael I. Jordan; Alan S. Willsky (ICML, 2008)

An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. - Percy Liang; Michael I. Jordan (ICML, 2008)

Efficient Inference in Phylogenetic InDel Trees. - Alexandre Bouchard-Côté; Michael I. Jordan; Dan Klein 001 (NIPS, 2008)

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. - Emily B. Fox; Erik B. Sudderth; Michael I. Jordan; Alan S. Willsky (NIPS, 2008)

Spectral Clustering with Perturbed Data. - Ling Huang 001; Donghui Yan; Michael I. Jordan; Nina Taft (NIPS, 2008)

DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. - Simon Lacoste-Julien; Fei Sha; Michael I. Jordan (NIPS, 2008)

High-dimensional support union recovery in multivariate regression. - Guillaume Obozinski; Martin J. Wainwright; Michael I. Jordan (NIPS, 2008)

Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. - Erik B. Sudderth; Michael I. Jordan (NIPS, 2008)

Posterior Consistency of the Silverman g-prior in Bayesian Model Choice. - Zhihua Zhang 004; Michael I. Jordan; Dit-Yan Yeung (NIPS, 2008)

Probabilistic Inference in Queueing Networks. - Charles Sutton; Michael I. Jordan (SysML, 2008)

Mining Console Logs for Large-Scale System Problem Detection. - Wei Xu 012; Ling Huang 001; Armando Fox; David A. Patterson 001; Michael I. Jordan (SysML, 2008)

On the Inference of Ancestries in Admixed Populations. - Sriram Sankararaman; Gad Kimmel; Eran Halperin; Michael I. Jordan (RECOMB, 2008)

The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. - Kurt T. Miller; Thomas L. Griffiths 001; Michael I. Jordan (UAI, 2008)

Graphical Models, Exponential Families, and Variational Inference. - Martin J. Wainwright; Michael I. Jordan (Found. Trends Mach. Learn., 2008)

A Dual Receptor Crosstalk Model of G-Protein-Coupled Signal Transduction. - Patrick Flaherty; Mala L. Radhakrishnan; Tuan Dinh; Robert A. Rebres; Tamara I. Roach; Michael I. Jordan; Adam P. Arkin (PLoS Comput. Biol., 2008)

On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection. - XuanLong Nguyen; Martin J. Wainwright; Michael I. Jordan (IEEE Trans. Inf. Theory, 2008)

Learning Semantic Correspondences with Less Supervision. - Percy Liang; Michael I. Jordan; Dan Klein 001 (ACL/IJCNLP, 2009)

Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters. - Peter Bodík; Rean Griffith; Charles Sutton; Armando Fox; Michael I. Jordan; David A. Patterson 001 (HotCloud, 2009)

Automatic exploration of datacenter performance regimes. - Peter Bodík; Rean Griffith; Charles Sutton; Armando Fox; Michael I. Jordan; David A. Patterson 001 (ACDC@ICAC, 2009)

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 001 (ICDE, 2009)

Online System Problem Detection by Mining Patterns of Console Logs. - Wei Xu 012; Ling Huang 001; Armando Fox; David A. Patterson 001; Michael I. Jordan (ICDM, 2009)

Learning from measurements in exponential families. - Percy Liang; Michael I. Jordan; Dan Klein 001 (ICML, 2009)

Fast approximate spectral clustering. - Donghui Yan; Ling Huang 001; Michael I. Jordan (KDD, 2009)

Sharing Features among Dynamical Systems with Beta Processes. - Emily B. Fox; Erik B. Sudderth; Michael I. Jordan; Alan S. Willsky (NIPS, 2009)

Asymptotically Optimal Regularization in Smooth Parametric Models. - Percy Liang; Francis R. Bach; Guillaume Bouchard; Michael I. Jordan (NIPS, 2009)

Nonparametric Latent Feature Models for Link Prediction. - Kurt T. Miller; Thomas L. Griffiths 001; Michael I. Jordan (NIPS, 2009)

A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. - Zhihua Zhang 004; Guang Dai; Michael I. Jordan (ECML/PKDD (2), 2009)

Combinatorial stochastic processes and nonparametric Bayesian modeling. - Michael I. Jordan (SODA, 2009)

Optimization of Structured Mean Field Objectives. - Alexandre Bouchard-Côté; Michael I. Jordan (UAI, 2009)

Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data. - Junming Yin; Michael I. Jordan; Yun S. Song (Bioinform., 2009)

Latent Variable Models for Dimensionality Reduction. - Zhihua Zhang 004; Michael I. Jordan (AISTATS, 2009)

Coherence Functions for Multicategory Margin-based Classification Methods. - Zhihua Zhang 004; Michael I. Jordan; Wu-Jun Li; Dit-Yan Yeung (AISTATS, 2009)

Characterizing, modeling, and generating workload spikes for stateful services. - Peter Bodík; Armando Fox; Michael J. Franklin; Michael I. Jordan; David A. Patterson 001 (SoCC, 2010)

Sufficient dimension reduction for visual sequence classification. - Alex Shyr; Raquel Urtasun; Michael I. Jordan (CVPR, 2010)

On the Consistency of Ranking Algorithms. - John C. Duchi; Lester W. Mackey; Michael I. Jordan (ICML, 2010)

Learning Programs: A Hierarchical Bayesian Approach. - Percy Liang; Michael I. Jordan; Dan Klein 001 (ICML, 2010)

Mixed Membership Matrix Factorization. - Lester W. Mackey; David J. Weiss; Michael I. Jordan (ICML, 2010)

Multiple Non-Redundant Spectral Clustering Views. - Donglin Niu; Jennifer G. Dy; Michael I. Jordan (ICML, 2010)

An Analysis of the Convergence of Graph Laplacians. - Daniel Ting; Ling Huang 001; Michael I. Jordan (ICML, 2010)

Detecting Large-Scale System Problems by Mining Console Logs. - Wei Xu 012; Ling Huang 001; Armando Fox; David A. Patterson 001; Michael I. Jordan (ICML, 2010)

Type-Based MCMC. - Percy Liang; Michael I. Jordan; Dan Klein 001 (HLT-NAACL, 2010)

Tree-Structured Stick Breaking for Hierarchical Data. - Ryan Prescott Adams; Zoubin Ghahramani; Michael I. Jordan (NIPS, 2010)

Variational Inference over Combinatorial Spaces. - Alexandre Bouchard-Côté; Michael I. Jordan (NIPS, 2010)

Random Conic Pursuit for Semidefinite Programming. - Ariel Kleiner; Ali Rahimi; Michael I. Jordan (NIPS, 2010)

Unsupervised Kernel Dimension Reduction. - Meihong Wang; Fei Sha; Michael I. Jordan (NIPS, 2010)

Heavy-Tailed Process Priors for Selective Shrinkage. - Fabian L. Wauthier; Michael I. Jordan (NIPS, 2010)

Experience Mining Google's Production Console Logs. - Wei Xu 012; Ling Huang 001; Michael I. Jordan (SLAML, 2010)

Modeling Events with Cascades of Poisson Processes. - Aleksandr Simma; Michael I. Jordan (UAI, 2010)

Active site prediction using evolutionary and structural information. - Sriram Sankararaman; Fei Sha; Jack F. Kirsch; Michael I. Jordan; Kimmen Sjölander (Bioinform., 2010)

Bayesian Inference in Queueing Networks - Charles Sutton; Michael I. Jordan (CoRR, 2010)

The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. - David M. Blei; Thomas L. Griffiths 001; Michael I. Jordan (J. ACM, 2010)

Inference and Learning in Networks of Queues. - Charles Sutton; Michael I. Jordan (AISTATS, 2010)

Matrix-Variate Dirichlet Process Mixture Models. - Zhihua Zhang 004; Guang Dai; Michael I. Jordan (AISTATS, 2010)

Bayesian Generalized Kernel Models. - Zhihua Zhang 004; Guang Dai; Donghui Wang; Michael I. Jordan (AISTATS, 2010)

Regularized Discriminant Analysis, Ridge Regression and Beyond. - Zhihua Zhang 004; Guang Dai; Congfu Xu; Michael I. Jordan (J. Mach. Learn. Res., 2010)

Convex and Semi-Nonnegative Matrix Factorizations. - Chris H. Q. Ding; Tao Li 001; Michael I. Jordan (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

Neighbor-Dependent Ramachandran Probability Distributions of Amino Acids Developed from a Hierarchical Dirichlet Process Model. - Daniel Ting; Guoli Wang 005; Maxim V. Shapovalov; Rajib Mitra; Michael I. Jordan; Roland L. Dunbrack Jr. (PLoS Comput. Biol., 2010)

Joint covariate selection and joint subspace selection for multiple classification problems. - Guillaume Obozinski; Ben Taskar; Michael I. Jordan (Stat. Comput., 2010)

Bayesian Nonparametric Methods for Learning Markov Switching Processes. - Emily B. Fox; Erik B. Sudderth; Michael I. Jordan; Alan S. Willsky (IEEE Signal Process. Mag., 2010)

Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization. - XuanLong Nguyen; Martin J. Wainwright; Michael I. Jordan (IEEE Trans. Inf. Theory, 2010)

Visually Relating Gene Expression and in vivo DNA Binding Data. - Min-Yu Huang; Lester W. Mackey; Soile V. E. Keränen; Gunther H. Weber; Michael I. Jordan; David W. Knowles; Mark D. Biggin; Bernd Hamann (BIBM, 2011)

Supervised hierarchical Pitman-Yor process for natural scene segmentation. - Alex Shyr; Trevor Darrell; Michael I. Jordan; Raquel Urtasun (CVPR, 2011)

The SCADS Director: Scaling a Distributed Storage System Under Stringent Performance Requirements. - Beth Trushkowsky; Peter Bodík; Armando Fox; Michael J. Franklin; Michael I. Jordan; David A. Patterson 001 (FAST, 2011)

A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection. - Yue Guan 001; Jennifer G. Dy; Michael I. Jordan (ICML, 2011)

Divide-and-Conquer Matrix Factorization. - Lester W. Mackey; Ameet Talwalkar; Michael I. Jordan (NIPS, 2011)

Bayesian Bias Mitigation for Crowdsourcing. - Fabian L. Wauthier; Michael I. Jordan (NIPS, 2011)

Nonparametric Bayesian Co-clustering Ensembles. - Pu Wang 002; Kathryn B. Laskey; Carlotta Domeniconi; Michael I. Jordan (SDM, 2011)

Managing data transfers in computer clusters with orchestra. - Mosharaf Chowdhury; Matei Zaharia; Justin Ma; Michael I. Jordan; Ion Stoica (SIGCOMM, 2011)

Non-parametric Link Prediction - Purnamrita Sarkar; Deepayan Chakrabarti; Michael I. Jordan (CoRR, 2011)

Nonparametric Combinatorial Sequence Models. - Fabian L. Wauthier; Michael I. Jordan; Nebojsa Jojic (J. Comput. Biol., 2011)

Dimensionality Reduction for Spectral Clustering. - Donglin Niu; Jennifer G. Dy; Michael I. Jordan (AISTATS, 2011)

Bayesian Generalized Kernel Mixed Models. - Zhihua Zhang 004; Guang Dai; Michael I. Jordan (J. Mach. Learn. Res., 2011)

Learning Low-Dimensional Signal Models. - Lawrence Carin; Richard G. Baraniuk; Volkan Cevher; David B. Dunson; Michael I. Jordan; Guillermo Sapiro; Michael B. Wakin (IEEE Signal Process. Mag., 2011)

Bayesian Nonparametric Inference of Switching Dynamic Linear Models. - Emily B. Fox; Erik B. Sudderth; Michael I. Jordan; Alan S. Willsky (IEEE Trans. Signal Process., 2011)

The Big Data Bootstrap. - Ariel Kleiner; Ameet Talwalkar; Purnamrita Sarkar; Michael I. Jordan (ICML, 2012)

Revisiting k-means: New Algorithms via Bayesian Nonparametrics. - Brian Kulis; Michael I. Jordan (ICML, 2012)

Variational Bayesian Inference with Stochastic Search. - John W. Paisley; David M. Blei; Michael I. Jordan (ICML, 2012)

Nonparametric Link Prediction in Dynamic Networks. - Purnamrita Sarkar; Deepayan Chakrabarti; Michael I. Jordan (ICML, 2012)

Divide-and-conquer and statistical inference for big data. - Michael I. Jordan (KDD, 2012)

Active spectral clustering via iterative uncertainty reduction. - Fabian L. Wauthier; Nebojsa Jojic; Michael I. Jordan (KDD, 2012)

Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods. - John C. Duchi; Michael I. Jordan; Martin J. Wainwright; Andre Wibisono (NIPS, 2012)

Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. - Ke Jiang; Brian Kulis; Michael I. Jordan (NIPS, 2012)

Ancestor Sampling for Particle Gibbs. - Fredrik Lindsten; Michael I. Jordan; Thomas B. Schön (NIPS, 2012)

The Asymptotics of Ranking Algorithms - John C. Duchi; Lester W. Mackey; Michael I. Jordan (CoRR, 2012)

Graph partition strategies for generalized mean field inference - Eric P. Xing; Michael I. Jordan; Stuart Russell 001 (CoRR, 2012)

Active Learning for Crowd-Sourced Databases - Barzan Mozafari; Purnamrita Sarkar; Michael J. Franklin; Michael I. Jordan; Samuel Madden 001 (CoRR, 2012)

Computational and Statistical Tradeoffs via Convex Relaxation - Venkat Chandrasekaran; Michael I. Jordan (CoRR, 2012)

Stick-Breaking Beta Processes and the Poisson Process. - John W. Paisley; David M. Blei; Michael I. Jordan (AISTATS, 2012)

Coherence functions with applications in large-margin classification methods. - Zhihua Zhang 004; Dehua Liu; Guang Dai; Michael I. Jordan (J. Mach. Learn. Res., 2012)

EP-GIG Priors and Applications in Bayesian Sparse Learning. - Zhihua Zhang 004; Shusen Wang; Dehua Liu; Michael I. Jordan (J. Mach. Learn. Res., 2012)

Qualcomm Context-Awareness Symposium Sets Research Agenda for Context-Aware Smartphones. - Paul Lukowicz; Sanjiv Nanda; Vidya Narayanan 003; Hal Abelson; Deborah L. McGuinness; Michael I. Jordan (IEEE Pervasive Comput., 2012)

Ergodic Mirror Descent. - John C. Duchi; Alekh Agarwal; Mikael Johansson; Michael I. Jordan (SIAM J. Optim., 2012)

MLbase: A Distributed Machine-learning System. - Tim Kraska; Ameet Talwalkar; John C. Duchi; Rean Griffith; Michael J. Franklin; Michael I. Jordan (CIDR, 2013)

Local Privacy and Statistical Minimax Rates. - John C. Duchi; Michael I. Jordan; Martin J. Wainwright (FOCS, 2013)

Distributed Low-Rank Subspace Segmentation. - Ameet Talwalkar; Lester W. Mackey; Yadong Mu; Shih-Fu Chang; Michael I. Jordan (ICCV, 2013)

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 (ICDM, 2013)

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. - Tamara Broderick; Brian Kulis; Michael I. Jordan (ICML (3), 2013)

Efficient Ranking from Pairwise Comparisons. - Fabian L. Wauthier; Michael I. Jordan; Nebojsa Jojic (ICML (3), 2013)

A general bootstrap performance diagnostic. - Ariel Kleiner; Ameet Talwalkar; Sameer Agarwal 002; Ion Stoica; Michael I. Jordan (KDD, 2013)

Streaming Variational Bayes. - Tamara Broderick; Nicholas Boyd; Andre Wibisono; Ashia C. Wilson; Michael I. Jordan (NIPS, 2013)

Estimation, Optimization, and Parallelism when Data is Sparse. - John C. Duchi; Michael I. Jordan; H. Brendan McMahan (NIPS, 2013)

Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation. - John C. Duchi; Martin J. Wainwright; Michael I. Jordan (NIPS, 2013)

Optimistic Concurrency Control for Distributed Unsupervised Learning. - Xinghao Pan; Joseph E. Gonzalez; Stefanie Jegelka; Tamara Broderick; Michael I. Jordan (NIPS, 2013)

A Comparative Framework for Preconditioned Lasso Algorithms. - Fabian L. Wauthier; Nebojsa Jojic; Michael I. Jordan (NIPS, 2013)

Information-theoretic lower bounds for distributed statistical estimation with communication constraints. - Yuchen Zhang 002; John C. Duchi; Michael I. Jordan; Martin J. Wainwright (NIPS, 2013)

Bayesian semiparametric Wiener system identification. - Fredrik Lindsten; Thomas B. Schön; Michael I. Jordan (Autom., 2013)

Learning Dependency-Based Compositional Semantics. - Percy Liang; Michael I. Jordan; Dan Klein 001 (Comput. Linguistics, 2013)

Optimal rates for zero-order optimization: the power of two function evaluations. - John C. Duchi; Michael I. Jordan; Martin J. Wainwright; Andre Wibisono (CoRR, 2013)

Mixed Membership Models for Time Series. - Emily B. Fox; Michael I. Jordan (CoRR, 2013)

On statistics, computation and scalability. - Michael I. Jordan (CoRR, 2013)

Variational MCMC - Nando de Freitas; Pedro A. d. F. R. Højen-Sørensen; Michael I. Jordan; Stuart Russell 001 (CoRR, 2013)

A Nested HDP for Hierarchical Topic Models - John W. Paisley; Chong Wang 002; David M. Blei; Michael I. Jordan (ICLR (Workshop), 2013)

Divide-and-Conquer Subspace Segmentation - Ameet Talwalkar; Lester W. Mackey; Yadong Mu; Shih-Fu Chang; Michael I. Jordan (CoRR, 2013)

Cluster Forests. - Donghui Yan; Aiyou Chen; Michael I. Jordan (Comput. Stat. Data Anal., 2013)

Neural Networks. - Michael I. Jordan; Christopher M. Bishop (Computing Handbook, 3rd ed. (1), 2014)

Lower bounds on the performance of polynomial-time algorithms for sparse linear regression. - Yuchen Zhang 002; Martin J. Wainwright; Michael I. Jordan (COLT, 2014)

Communication-Efficient Distributed Dual Coordinate Ascent. - Martin Jaggi; Virginia Smith; Martin Takác 001; Jonathan Terhorst; Sanjay Krishnan; Thomas Hofmann 001; Michael I. Jordan (NIPS, 2014)

On the Convergence Rate of Decomposable Submodular Function Minimization. - Robert Nishihara; Stefanie Jegelka; Michael I. Jordan (NIPS, 2014)

Parallel Double Greedy Submodular Maximization. - Xinghao Pan; Stefanie Jegelka; Joseph E. Gonzalez; Joseph K. Bradley; Michael I. Jordan (NIPS, 2014)

Changepoint Analysis for Efficient Variant Calling. - Adam E. Bloniarz; Ameet Talwalkar; Jonathan Terhorst; Michael I. Jordan; David A. Patterson 001; Bin Yu 001; Yun S. Song (RECOMB, 2014)

Knowing when you're wrong: building fast and reliable approximate query processing systems. - Sameer Agarwal 002; Henry Milner; Ariel Kleiner; Ameet Talwalkar; Michael I. Jordan; Samuel Madden 001; Barzan Mozafari; Ion Stoica (SIGMOD Conference, 2014)

SMaSH: a benchmarking toolkit for human genome variant calling. - Ameet Talwalkar; Jesse Liptrap; Julie Newcomb; Christopher Hartl; Jonathan Terhorst; Kristal Curtis; Ma'ayan Bresler; Yun S. Song; Michael I. Jordan; David A. Patterson 001 (Bioinform., 2014)

Privacy Aware Learning. - John C. Duchi; Michael I. Jordan; Martin J. Wainwright (J. ACM, 2014)

Particle gibbs with ancestor sampling. - Fredrik Lindsten; Michael I. Jordan; Thomas B. Schön (J. Mach. Learn. Res., 2014)

Iterative Discovery of Multiple AlternativeClustering Views. - Donglin Niu; Jennifer G. Dy; Michael I. Jordan (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning. - Barzan Mozafari; Purnamrita Sarkar; Michael J. Franklin; Michael I. Jordan; Samuel Madden 001 (Proc. VLDB Endow., 2014)

Mixed Membership Models for Time Series. - Emily B. Fox; Michael I. Jordan (Handbook of Mixed Membership Models and Their Applications, 2014)

Mixed Membership Matrix Factorization. - Lester W. Mackey; David J. Weiss; Michael I. Jordan (Handbook of Mixed Membership Models and Their Applications, 2014)

Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference. - John W. Paisley; David M. Blei; Michael I. Jordan (Handbook of Mixed Membership Models and Their Applications, 2014)

The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. - Daniel Crankshaw; Peter Bailis; Joseph E. Gonzalez; Haoyuan Li 001; Zhao Zhang 007; Michael J. Franklin; Ali Ghodsi 002; Michael I. Jordan (CIDR, 2015)

Automating model search for large scale machine learning. - Evan Randall Sparks; Ameet Talwalkar; Daniel Haas; Michael J. Franklin; Michael I. Jordan; Tim Kraska (SoCC, 2015)

Learning Transferable Features with Deep Adaptation Networks. - Mingsheng Long; Yue Cao 001; Jianmin Wang 001; Michael I. Jordan (ICML, 2015)

Adding vs. Averaging in Distributed Primal-Dual Optimization. - Chenxin Ma; Virginia Smith; Martin Jaggi; Michael I. Jordan; Peter Richtárik; Martin Takác 001 (ICML, 2015)

A General Analysis of the Convergence of ADMM. - Robert Nishihara; Laurent Lessard; Benjamin Recht; Andrew K. Packard; Michael I. Jordan (ICML, 2015)

Trust Region Policy Optimization. - John Schulman; Sergey Levine; Pieter Abbeel; Michael I. Jordan; Philipp Moritz (ICML, 2015)

Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds. - Yuchen Zhang 002; Martin J. Wainwright; Michael I. Jordan (ICML, 2015)

Optimism-driven exploration for nonlinear systems. - Teodor Mihai Moldovan; Sergey Levine; Michael I. Jordan; Pieter Abbeel (ICRA, 2015)

On the Accuracy of Self-Normalized Log-Linear Models. - Jacob Andreas; Maxim Rabinovich; Michael I. Jordan; Dan Klein 001 (NIPS, 2015)

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes. - Ryan Giordano; Tamara Broderick; Michael I. Jordan (NIPS, 2015)

Parallel Correlation Clustering on Big Graphs. - Xinghao Pan; Dimitris S. Papailiopoulos; Samet Oymak; Benjamin Recht; Kannan Ramchandran; Michael I. Jordan (NIPS, 2015)

Variational Consensus Monte Carlo. - Maxim Rabinovich; Elaine Angelino; Michael I. Jordan (NIPS, 2015)

Computational Thinking, Inferential Thinking and "Big Data". - Michael I. Jordan (PODS, 2015)

Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype? - Christopher Ré; Divy Agrawal; Magdalena Balazinska; Michael J. Cafarella; Michael I. Jordan; Tim Kraska; Raghu Ramakrishnan 001 (SIGMOD Conference, 2015)

Asynchronous Complex Analytics in a Distributed Dataflow Architecture. - Joseph E. Gonzalez; Peter Bailis; Michael I. Jordan; Michael J. Franklin; Joseph M. Hellerstein; Ali Ghodsi 002; Ion Stoica (CoRR, 2015)

L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework. - Virginia Smith; Simone Forte; Michael I. Jordan; Martin Jaggi (CoRR, 2015)

TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries. - Evan Randall Sparks; Ameet Talwalkar; Michael J. Franklin; Michael I. Jordan; Tim Kraska (CoRR, 2015)

On the Computational Complexity of High-Dimensional Bayesian Variable Selection. - Yun Yang; Martin J. Wainwright; Michael I. Jordan (CoRR, 2015)

Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms. - Yuchen Zhang 002; Michael I. Jordan (CoRR, 2015)

ℓ1-regularized Neural Networks are Improperly Learnable in Polynomial Time. - Yuchen Zhang 002; Jason D. Lee; Michael I. Jordan (CoRR, 2015)

Learning Halfspaces and Neural Networks with Random Initialization. - Yuchen Zhang 002; Jason D. Lee; Martin J. Wainwright; Michael I. Jordan (CoRR, 2015)

Distributed matrix completion and robust factorization. - Lester W. Mackey; Ameet Talwalkar; Michael I. Jordan (J. Mach. Learn. Res., 2015)

Combinatorial Clustering and the Beta Negative Binomial Process. - Tamara Broderick; Lester W. Mackey; John W. Paisley; Michael I. Jordan (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Nested Hierarchical Dirichlet Processes. - John W. Paisley; Chong Wang 002; David M. Blei; Michael I. Jordan (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations. - John C. Duchi; Michael I. Jordan; Martin J. Wainwright; Andre Wibisono (IEEE Trans. Inf. Theory, 2015)

The Constrained Laplacian Rank Algorithm for Graph-Based Clustering. - Feiping Nie 001; Xiaoqian Wang 001; Michael I. Jordan; Heng Huang (AAAI, 2016)

A Linearly-Convergent Stochastic L-BFGS Algorithm. - Philipp Moritz; Robert Nishihara; Michael I. Jordan (AISTATS, 2016)

Gradient Descent Only Converges to Minimizers. - Jason D. Lee; Max Simchowitz; Michael I. Jordan; Benjamin Recht (COLT, 2016)

A Kernelized Stein Discrepancy for Goodness-of-fit Tests. - Qiang Liu 001; Jason D. Lee; Michael I. Jordan (ICML, 2016)

L1-regularized Neural Networks are Improperly Learnable in Polynomial Time. - Yuchen Zhang 002; Jason D. Lee; Michael I. Jordan (ICML, 2016)

Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. - Chi Jin 001; Yuchen Zhang 002; Sivaraman Balakrishnan; Martin J. Wainwright; Michael I. Jordan (NIPS, 2016)

Unsupervised Domain Adaptation with Residual Transfer Networks. - Mingsheng Long; Han Zhu 007; Jianmin Wang 001; Michael I. Jordan (NIPS, 2016)

On Computational Thinking, Inferential Thinking and Data Science. - Michael I. Jordan (SPAA, 2016)

Asymptotic behavior of ℓp-based Laplacian regularization in semi-supervised learning. - Ahmed El Alaoui; Xiang Cheng 006; Aaditya Ramdas; Martin J. Wainwright; Michael I. Jordan (CoRR, 2016)

Minimax Optimal Procedures for Locally Private Estimation. - John C. Duchi; Martin J. Wainwright; Michael I. Jordan (CoRR, 2016)

Communication-efficient distributed statistical learning. - Michael I. Jordan; Jason D. Lee; Yun Yang (CoRR, 2016)

Gradient Descent Converges to Minimizers. - Jason D. Lee; Max Simchowitz; Michael I. Jordan; Benjamin Recht (CoRR, 2016)

Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method. - Lihua Lei; Michael I. Jordan (CoRR, 2016)

Unsupervised Domain Adaptation with Residual Transfer Networks. - Mingsheng Long; Jianmin Wang 001; Michael I. Jordan (CoRR, 2016)

Deep Transfer Learning with Joint Adaptation Networks. - Mingsheng Long; Jianmin Wang 001; Michael I. Jordan (CoRR, 2016)

Universality of Mallows' and degeneracy of Kendall's kernels for rankings. - Horia Mania; Aaditya Ramdas; Martin J. Wainwright; Michael I. Jordan; Benjamin Recht (CoRR, 2016)

SparkNet: Training Deep Networks in Spark. - Philipp Moritz; Robert Nishihara; Ion Stoica; Michael I. Jordan (ICLR (Poster), 2016)

CYCLADES: Conflict-free Asynchronous Machine Learning. - Xinghao Pan; Maximilian Lam; Stephen Tu; Dimitris S. Papailiopoulos; Ce Zhang 001; Michael I. Jordan; Kannan Ramchandran; Christopher Ré; Benjamin Recht (CoRR, 2016)

Function-Specific Mixing Times and Concentration Away from Equilibrium. - Maxim Rabinovich; Aaditya Ramdas; Michael I. Jordan; Martin J. Wainwright (CoRR, 2016)

High-Dimensional Continuous Control Using Generalized Advantage Estimation. - John Schulman; Philipp Moritz; Sergey Levine; Michael I. Jordan; Pieter Abbeel (ICLR (Poster), 2016)

A Variational Perspective on Accelerated Methods in Optimization. - Andre Wibisono; Ashia C. Wilson; Michael I. Jordan (CoRR, 2016)

A Lyapunov Analysis of Momentum Methods in Optimization. - Ashia C. Wilson; Benjamin Recht; Michael I. Jordan (CoRR, 2016)

Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing. - Yuchen Zhang 002; Xi Chen; Dengyong Zhou; Michael I. Jordan (J. Mach. Learn. Res., 2016)

Less than a Single Pass: Stochastically Controlled Stochastic Gradient. - Lihua Lei; Michael I. Jordan (AISTATS, 2017)

On the Learnability of Fully-Connected Neural Networks. - Yuchen Zhang 002; Jason D. Lee; Martin J. Wainwright; Michael I. Jordan (AISTATS, 2017)

QuTE: Decentralized multiple testing on sensor networks with false discovery rate control. - Aaditya Ramdas; Jianbo Chen; Martin J. Wainwright; Michael I. Jordan (CDC, 2017)

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 (HotOS, 2017)

How to Escape Saddle Points Efficiently. - Chi Jin 001; Rong Ge 001; Praneeth Netrapalli; Sham M. Kakade; Michael I. Jordan (ICML, 2017)

Deep Transfer Learning with Joint Adaptation Networks. - Mingsheng Long; Han Zhu 007; Jianmin Wang 001; Michael I. Jordan (ICML, 2017)

Breaking Locality Accelerates Block Gauss-Seidel. - Stephen Tu; Shivaram Venkataraman; Ashia C. Wilson; Alex Gittens; Michael I. Jordan; Benjamin Recht (ICML, 2017)

Kernel Feature Selection via Conditional Covariance Minimization. - Jianbo Chen; Mitchell Stern; Martin J. Wainwright; Michael I. Jordan (NIPS, 2017)

Gradient Descent Can Take Exponential Time to Escape Saddle Points. - Simon S. Du; Chi Jin 001; Jason D. Lee; Michael I. Jordan; Aarti Singh; Barnabás Póczos (NIPS, 2017)

Non-convex Finite-Sum Optimization Via SCSG Methods. - Lihua Lei; Cheng Ju; Jianbo Chen; Michael I. Jordan (NIPS, 2017)

Online control of the false discovery rate with decaying memory. - Aaditya Ramdas; Fanny Yang; Martin J. Wainwright; Michael I. Jordan (NIPS, 2017)

Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. - Jeffrey Regier; Michael I. Jordan; Jon McAuliffe (NIPS, 2017)

On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic. - Michael I. Jordan (SIGMETRICS (Abstracts), 2017)

Nonconvex Finite-Sum Optimization Via SCSG Methods. - Lihua Lei; Cheng Ju; Jianbo Chen; Michael I. Jordan (CoRR, 2017)

Domain Adaptation with Randomized Multilinear Adversarial Networks. - Mingsheng Long; Zhangjie Cao; Jianmin Wang 001; Michael I. Jordan (CoRR, 2017)

A deep generative model for gene expression profiles from single-cell RNA sequencing. - Romain Lopez; Jeffrey Regier; Michael I. Jordan; Nir Yosef (CoRR, 2017)

DAGGER: A sequential algorithm for FDR control on DAGs. - Aaditya Ramdas; Jianbo Chen; Martin J. Wainwright; Michael I. Jordan (CoRR, 2017)

Finite Size Corrections and Likelihood Ratio Fluctuations in the Spiked Wigner Model. - Ahmed El Alaoui; Florent Krzakala; Michael I. Jordan (CoRR, 2017)

First-order Methods Almost Always Avoid Saddle Points. - Jason D. Lee; Ioannis Panageas; Georgios Piliouras; Max Simchowitz; Michael I. Jordan; Benjamin Recht (CoRR, 2017)

A Berkeley View of Systems Challenges for AI. - Ion Stoica; Dawn Song; Raluca Ada Popa; David A. Patterson 001; Michael W. Mahoney; Randy H. Katz; Anthony D. Joseph; Michael I. Jordan; Joseph M. Hellerstein; Joseph E. Gonzalez; Ken Goldberg; Ali Ghodsi 002; et al. (CoRR, 2017)

Ray: A Distributed Framework for Emerging AI Applications. - Philipp Moritz; Robert Nishihara; Stephanie Wang; Alexey Tumanov; Richard Liaw; Eric Liang; William Paul; Michael I. Jordan; Ion Stoica (CoRR, 2017)

Saturating Splines and Feature Selection. - Nicholas Boyd; Trevor Hastie; Stephen P. Boyd; Benjamin Recht; Michael I. Jordan (J. Mach. Learn. Res., 2017)

CoCoA: A General Framework for Communication-Efficient Distributed Optimization. - Virginia Smith; Simone Forte; Chenxin Ma; Martin Takác 001; Michael I. Jordan; Martin Jaggi (J. Mach. Learn. Res., 2017)

Distributed optimization with arbitrary local solvers. - Chenxin Ma; Jakub Konecný; Martin Jaggi; Virginia Smith; Michael I. Jordan; Peter Richtárik; Martin Takác 001 (Optim. Methods Softw., 2017)

Perturbed Iterate Analysis for Asynchronous Stochastic Optimization. - Horia Mania; Xinghao Pan; Dimitris S. Papailiopoulos; Benjamin Recht; Kannan Ramchandran; Michael I. Jordan (SIAM J. Optim., 2017)

A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin. - Sindhu Ghanta; Michael I. Jordan; Kivanç Köse; Dana H. Brooks; Milind Rajadhyaksha; Jennifer G. Dy (IEEE Trans. Image Process., 2017)

Machine learning: trends, perspectives and challenges. - Michael I. Jordan (TURC, 2018)

Detection limits in the high-dimensional spiked rectangular model. - Ahmed El Alaoui; Michael I. Jordan (COLT, 2018)

Underdamped Langevin MCMC: A non-asymptotic analysis. - Xiang Cheng 006; Niladri S. Chatterji; Peter L. Bartlett; Michael I. Jordan (COLT, 2018)

Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. - Chi Jin 001; Praneeth Netrapalli; Michael I. Jordan (COLT, 2018)

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification. - Max Simchowitz; Horia Mania; Stephen Tu; Michael I. Jordan; Benjamin Recht (COLT, 2018)

Averaging Stochastic Gradient Descent on Riemannian Manifolds. - Nilesh Tripuraneni; Nicolas Flammarion; Francis R. Bach; Michael I. Jordan (COLT, 2018)

Partial Transfer Learning With Selective Adversarial Networks. - Zhangjie Cao; Mingsheng Long; Jianmin Wang 001; Michael I. Jordan (CVPR, 2018)

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 (ICML, 2018)

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. - Jianbo Chen; Le Song; Martin J. Wainwright; Michael I. Jordan (ICML, 2018)

RLlib: Abstractions for Distributed Reinforcement Learning. - Eric Liang; Richard Liaw; Robert Nishihara; Philipp Moritz; Roy Fox; Ken Goldberg; Joseph Gonzalez 001; Michael I. Jordan; Ion Stoica (ICML, 2018)

SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate. - Aaditya Ramdas; Tijana Zrnic; Martin J. Wainwright; Michael I. Jordan (ICML, 2018)

Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. - Kush Bhatia; Aldo Pacchiano; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (NeurIPS, 2018)

Theoretical guarantees for EM under misspecified Gaussian mixture models. - Raaz Dwivedi; Nhat Ho; Koulik Khamaru; Martin J. Wainwright; Michael I. Jordan (NeurIPS, 2018)

Is Q-Learning Provably Efficient? - Chi Jin 001; Zeyuan Allen-Zhu; Sébastien Bubeck; Michael I. Jordan (NeurIPS, 2018)

On the Local Minima of the Empirical Risk. - Chi Jin 001; Lydia T. Liu; Rong Ge 001; Michael I. Jordan (NeurIPS, 2018)

Generalized Zero-Shot Learning with Deep Calibration Network. - Shichen Liu; Mingsheng Long; Jianmin Wang 001; Michael I. Jordan (NeurIPS, 2018)

Conditional Adversarial Domain Adaptation. - Mingsheng Long; Zhangjie Cao; Jianmin Wang 001; Michael I. Jordan (NeurIPS, 2018)

Information Constraints on Auto-Encoding Variational Bayes. - Romain Lopez; Jeffrey Regier; Michael I. Jordan; Nir Yosef (NeurIPS, 2018)

Stochastic Cubic Regularization for Fast Nonconvex Optimization. - Nilesh Tripuraneni; Mitchell Stern; Chi Jin 001; Jeffrey Regier; Michael I. Jordan (NeurIPS, 2018)

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 (OSDI, 2018)

Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning. - Vladimir Feinberg; Alvin Wan; Ion Stoica; Michael I. Jordan; Joseph E. Gonzalez; Sergey Levine (CoRR, 2018)

Minimizing Nonconvex Population Risk from Rough Empirical Risk. - Chi Jin 001; Lydia T. Liu; Rong Ge 001; Michael I. Jordan (CoRR, 2018)

Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting. - Xiang Cheng 006; Niladri S. Chatterji; Yasin Abbasi-Yadkori; Peter L. Bartlett; Michael I. Jordan (CoRR, 2018)

Improved Oracle Complexity for Stochastic Compositional Variance Reduced Gradient. - Tianyi Lin; Chenyou Fan; Mengdi Wang; Michael I. Jordan (CoRR, 2018)

A Deep Generative Model for Semi-Supervised Classification with Noisy Labels. - Maxime Langevin; Edouard Mehlman; Jeffrey Regier; Romain Lopez; Michael I. Jordan; Nir Yosef (CoRR, 2018)

Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. - Nhat Ho; Tan M. Nguyen; Ankit B. Patel; Anima Anandkumar; Michael I. Jordan; Richard G. Baraniuk (CoRR, 2018)

Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation. - Kush Bhatia; Aldo Pacchiano; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (CoRR, 2018)

Sampling Can Be Faster Than Optimization. - Yi-An Ma; Yuansi Chen; Chi Jin 001; Nicolas Flammarion; Michael I. Jordan (CoRR, 2018)

Covariances, Robustness, and Variational Bayes. - Ryan Giordano; Tamara Broderick; Michael I. Jordan (J. Mach. Learn. Res., 2018)

A Swiss Army Infinitesimal Jackknife. - Ryan Giordano; William T. Stephenson; Runjing Liu; Michael I. Jordan; Tamara Broderick (AISTATS, 2019)

Probabilistic Multilevel Clustering via Composite Transportation Distance. - Nhat Ho; Viet Huynh; Dinh Q. Phung; Michael I. Jordan (AISTATS, 2019)

Universal Domain Adaptation. - Kaichao You; Mingsheng Long; Zhangjie Cao; Jianmin Wang 001; Michael I. Jordan (CVPR, 2019)

L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data. - Jianbo Chen; Le Song; Martin J. Wainwright; Michael I. Jordan (ICLR (Poster), 2019)

Bridging Theory and Algorithm for Domain Adaptation. - Yuchen Zhang 004; Tianle Liu; Mingsheng Long; Michael I. Jordan (ICML, 2019)

On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. - Tianyi Lin; Nhat Ho; Michael I. Jordan (ICML, 2019)

Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers. - Hong Liu; Mingsheng Long; Jianmin Wang 001; Michael I. Jordan (ICML, 2019)

Rao-Blackwellized Stochastic Gradients for Discrete Distributions. - Runjing Liu; Jeffrey Regier; Nilesh Tripuraneni; Michael I. Jordan; Jon D. McAuliffe (ICML, 2019)

A Dynamical Systems Perspective on Nesterov Acceleration. - Michael Muehlebach; Michael I. Jordan (ICML, 2019)

Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. - Kaichao You; Ximei Wang; Mingsheng Long; Michael I. Jordan (ICML, 2019)

Theoretically Principled Trade-off between Robustness and Accuracy. - Hongyang Zhang 001; Yaodong Yu; Jiantao Jiao; Eric P. Xing; Laurent El Ghaoui; Michael I. Jordan (ICML, 2019)

Acceleration via Symplectic Discretization of High-Resolution Differential Equations. - Bin Shi; Simon S. Du; Weijie J. Su; Michael I. Jordan (NeurIPS, 2019)

Transferable Normalization: Towards Improving Transferability of Deep Neural Networks. - Ximei Wang; Ying Jin; Mingsheng Long; Jianmin Wang 001; Michael I. Jordan (NeurIPS, 2019)

On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games. - Eric Mazumdar; Michael I. Jordan; S. Shankar Sastry (CoRR, 2019)

Challenges with EM in application to weakly identifiable mixture models. - Raaz Dwivedi; Nhat Ho; Koulik Khamaru; Martin J. Wainwright; Michael I. Jordan; Bin Yu 001 (CoRR, 2019)

Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. - Chi Jin 001; Praneeth Netrapalli; Michael I. Jordan (CoRR, 2019)

Quantitative Central Limit Theorems for Discrete Stochastic Processes. - Xiang Cheng 006; Peter L. Bartlett; Michael I. Jordan (CoRR, 2019)

Is There an Analog of Nesterov Acceleration for MCMC? - Yi-An Ma; Niladri S. Chatterji; Xiang Cheng 006; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (CoRR, 2019)

A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (CoRR, 2019)

Stochastic Gradient Descent Escapes Saddle Points Efficiently. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (CoRR, 2019)

Boundary Attack++: Query-Efficient Decision-Based Adversarial Attack. - Jianbo Chen; Michael I. Jordan (CoRR, 2019)

SysML: The New Frontier of Machine Learning Systems. - 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; et al. (CoRR, 2019)

Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and 𝓁1-Convex Clustering. - Nhat Ho; Tianyi Lin; Michael I. Jordan (CoRR, 2019)

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. - Romain Lopez; Achille Nazaret; Maxime Langevin; Jules Samaran; Jeffrey Regier; Michael I. Jordan; Nir Yosef (CoRR, 2019)

Accelerated Primal-Dual Coordinate Descent for Computational Optimal Transport. - Wenshuo Guo; Nhat Ho; Michael I. Jordan (CoRR, 2019)

Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models. - Chiao-Yu Yang; Nhat Ho; Michael I. Jordan (CoRR, 2019)

On the Acceleration of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport. - Tianyi Lin; Nhat Ho; Michael I. Jordan (CoRR, 2019)

Wasserstein Reinforcement Learning. - Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Anna Choromanska; Krzysztof Choromanski; Michael I. Jordan (CoRR, 2019)

Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization. - Jonathan N. Lee; Aldo Pacchiano; Michael I. Jordan (CoRR, 2019)

Quantitative W1 Convergence of Langevin-Like Stochastic Processes with Non-Convex Potential State-Dependent Noise. - Xiang Cheng 006; Dong Yin; Peter L. Bartlett; Michael I. Jordan (CoRR, 2019)

Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings. - Eric Mazumdar; Lillian J. Ratliff; Michael I. Jordan; S. Shankar Sastry (CoRR, 2019)

Bayesian Robustness: A Nonasymptotic Viewpoint. - Kush Bhatia; Yi-An Ma; Anca D. Dragan; Peter L. Bartlett; Michael I. Jordan (CoRR, 2019)

A Higher-Order Swiss Army Infinitesimal Jackknife. - Ryan Giordano; Michael I. Jordan; Tamara Broderick (CoRR, 2019)

Learning Stages: Phenomenon, Root Cause, Mechanism Hypothesis, and Implications. - Kaichao You; Mingsheng Long; Michael I. Jordan; Jianmin Wang 001 (CoRR, 2019)

Towards Understanding the Transferability of Deep Representations. - Hong Liu; Mingsheng Long; Jianmin Wang 001; Michael I. Jordan (CoRR, 2019)

Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing. - Wenlong Mou; Nhat Ho; Martin J. Wainwright; Peter L. Bartlett; Michael I. Jordan (CoRR, 2019)

First-order methods almost always avoid strict saddle points. - Jason D. Lee; Ioannis Panageas; Georgios Piliouras; Max Simchowitz; Michael I. Jordan; Benjamin Recht (Math. Program., 2019)

Transferable Representation Learning with Deep Adaptation Networks. - Mingsheng Long; Yue Cao 001; Zhangjie Cao; Jianmin Wang 001; Michael I. Jordan (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

Decoding from Pooled Data: Sharp Information-Theoretic Bounds. - Ahmed El Alaoui; Aaditya Ramdas; Florent Krzakala; Lenka Zdeborová; Michael I. Jordan (SIAM J. Math. Data Sci., 2019)

Decoding From Pooled Data: Phase Transitions of Message Passing. - Ahmed El Alaoui; Aaditya Ramdas; Florent Krzakala; Lenka Zdeborová; Michael I. Jordan (IEEE Trans. Inf. Theory, 2019)

LS-Tree: Model Interpretation When the Data Are Linguistic. - Jianbo Chen; Michael I. Jordan (AAAI, 2020)

Cost-Effective Incentive Allocation via Structured Counterfactual Inference. - Romain Lopez; Chenchen Li; Xiang Yan; Junwu Xiong; Michael I. Jordan; Yuan Qi 001; Le Song (AAAI, 2020)

ML-LOO: Detecting Adversarial Examples with Feature Attribution. - Puyudi Yang; Jianbo Chen; Cho-Jui Hsieh; Jane-Ling Wang; Michael I. Jordan (AAAI, 2020)

Langevin Monte Carlo without smoothness. - Niladri S. Chatterji; Jelena Diakonikolas; Michael I. Jordan; Peter L. Bartlett (AISTATS, 2020)

Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models. - Raaz Dwivedi; Nhat Ho; Koulik Khamaru; Martin J. Wainwright; Michael I. Jordan; Bin Yu 001 (AISTATS, 2020)

Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter. - Wenshuo Guo; Nhat Ho; Michael I. Jordan (AISTATS, 2020)

Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. - Jonathan N. Lee; Aldo Pacchiano; Michael I. Jordan (AISTATS, 2020)

Competing Bandits in Matching Markets. - Lydia T. Liu; Horia Mania; Michael I. Jordan (AISTATS, 2020)

Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information. - Esther Rolf; Michael I. Jordan; Benjamin Recht (AISTATS, 2020)

The Power of Batching in Multiple Hypothesis Testing. - Tijana Zrnic; Daniel L. Jiang; Aaditya Ramdas; Michael I. Jordan (AISTATS, 2020)

Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient. - Tianyi Lin; Chengyou Fan; Mengdi Wang; Michael I. Jordan (ACC, 2020)

Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games. - Eric Mazumdar; Lillian J. Ratliff; Michael I. Jordan; S. Shankar Sastry (AAMAS, 2020)

High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. - Eric Mazumdar; Tyler Westenbroek; Michael I. Jordan; S. Shankar Sastry (CDC, 2020)

Near-Optimal Algorithms for Minimax Optimization. - Tianyi Lin; Chi Jin 001; Michael I. Jordan (COLT, 2020)

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 (COLT, 2020)

Unsupervised Online Learning for Long-Term High Sensitivity Seizure Detection. - Adelson Chua; Michael I. Jordan; Rikky Muller (EMBC, 2020)

Variance Reduction With Sparse Gradients. - Melih Elibol; Lihua Lei; Michael I. Jordan (ICLR, 2020)

Stochastic Gradient and Langevin Processes. - Xiang Cheng 006; Dong Yin; Peter L. Bartlett; Michael I. Jordan (ICML, 2020)

What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? - Chi Jin 001; Praneeth Netrapalli; Michael I. Jordan (ICML, 2020)

Accelerated Message Passing for Entropy-Regularized MAP Inference. - Jonathan N. Lee; Aldo Pacchiano; Peter L. Bartlett; Michael I. Jordan (ICML, 2020)

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. - Tianyi Lin; Chi Jin 001; Michael I. Jordan (ICML, 2020)

Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. - Tianyi Lin; Zhengyuan Zhou; Panayotis Mertikopoulos; Michael I. Jordan (ICML, 2020)

On Approximate Thompson Sampling with Langevin Algorithms. - Eric Mazumdar; Aldo Pacchiano; Yi-An Ma; Michael I. Jordan; Peter L. Bartlett (ICML, 2020)

Continuous-time Lower Bounds for Gradient-based Algorithms. - Michael Muehlebach; Michael I. Jordan (ICML, 2020)

Learning to Score Behaviors for Guided Policy Optimization. - Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Krzysztof Choromanski; Anna Choromanska; Michael I. Jordan (ICML, 2020)

Projection Robust Wasserstein Distance and Riemannian Optimization. - Tianyi Lin; Chenyou Fan; Nhat Ho; Marco Cuturi; Michael I. Jordan (NeurIPS, 2020)

Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. - Tianyi Lin; Nhat Ho; Xi Chen; Marco Cuturi; Michael I. Jordan (NeurIPS, 2020)

Decision-Making with Auto-Encoding Variational Bayes. - Romain Lopez; Pierre Boyeau; Nir Yosef; Michael I. Jordan; Jeffrey Regier (NeurIPS, 2020)

On the Theory of Transfer Learning: The Importance of Task Diversity. - Nilesh Tripuraneni; Michael I. Jordan; Chi Jin 001 (NeurIPS, 2020)

Robust Optimization for Fairness with Noisy Protected Groups. - Serena Lutong Wang; Wenshuo Guo; Harikrishna Narasimhan; Andrew Cotter; Maya R. Gupta; Michael I. Jordan (NeurIPS, 2020)

Transferable Calibration with Lower Bias and Variance in Domain Adaptation. - Ximei Wang; Mingsheng Long; Jianmin Wang 001; Michael I. Jordan (NeurIPS, 2020)

Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. - Zhuoran Yang; Chi Jin 001; Zhaoran Wang 001; Mengdi Wang; Michael I. Jordan (NeurIPS, 2020)

HopSkipJumpAttack: A Query-Efficient Decision-Based Attack. - Jianbo Chen; Michael I. Jordan; Martin J. Wainwright (SP, 2020)

Revisiting Fixed Support Wasserstein Barycenter: Computational Hardness and Efficient Algorithms. - Tianyi Lin; Nhat Ho; Xi Chen; Marco Cuturi; Michael I. Jordan (CoRR, 2020)

On Thompson Sampling with Langevin Algorithms. - Eric Mazumdar; Aldo Pacchiano; Yi-An Ma; Peter L. Bartlett; Michael I. Jordan (CoRR, 2020)

Mechanism Design with Bandit Feedback. - Kirthevasan Kandasamy; Joseph E. Gonzalez; Michael I. Jordan; Ion Stoica (CoRR, 2020)

Lower bounds in multiple testing: A framework based on derandomized proxies. - Max Rabinovich; Michael I. Jordan; Martin J. Wainwright (CoRR, 2020)

Instability, Computational Efficiency and Statistical Accuracy. - Nhat Ho; Koulik Khamaru; Raaz Dwivedi; Martin J. Wainwright; Michael I. Jordan; Bin Yu 001 (CoRR, 2020)

Manifold Learning via Manifold Deflation. - Daniel Ting; Michael I. Jordan (CoRR, 2020)

Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty. - Wenshuo Guo; Mihaela Curmei; Serena Lutong Wang; Benjamin Recht; Michael I. Jordan (CoRR, 2020)

Optimal Robust Linear Regression in Nearly Linear Time. - Yeshwanth Cherapanamjeri; Efe Aras; Nilesh Tripuraneni; Michael I. Jordan; Nicolas Flammarion; Peter L. Bartlett (CoRR, 2020)

On Localized Discrepancy for Domain Adaptation. - Yuchen Zhang 004; Mingsheng Long; Jianmin Wang 001; Michael I. Jordan (CoRR, 2020)

Exploration in two-stage recommender systems. - Jiri Hron; Karl Krauth; Michael I. Jordan; Niki Kilbertus (CoRR, 2020)

Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism. - Brijen Thananjeyan; Kirthevasan Kandasamy; Ion Stoica; Michael I. Jordan; Ken Goldberg; Joseph E. Gonzalez (CoRR, 2020)

Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations. - Zhuoran Yang; Chi Jin 001; Zhaoran Wang 001; Mengdi Wang; Michael I. Jordan (CoRR, 2020)

Do Offline Metrics Predict Online Performance in Recommender Systems? - Karl Krauth; Sarah Dean; Alex Zhao; Wenshuo Guo; Mihaela Curmei; Benjamin Recht; Michael I. Jordan (CoRR, 2020)

Online Learning Demands in Max-min Fairness. - Kirthevasan Kandasamy; Gur-Eyal Sela; Joseph E. Gonzalez; Michael I. Jordan; Ion Stoica (CoRR, 2020)

Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. - Puyudi Yang; Jianbo Chen; Cho-Jui Hsieh; Jane-Ling Wang; Michael I. Jordan (J. Mach. Learn. Res., 2020)

On the Adaptivity of Stochastic Gradient-Based Optimization. - Lihua Lei; Michael I. Jordan (SIAM J. Optim., 2020)

Learning from eXtreme Bandit Feedback. - Romain Lopez; Inderjit S. Dhillon; Michael I. Jordan (AAAI, 2021)

Robustness Guarantees for Mode Estimation with an Application to Bandits. - Aldo Pacchiano; Heinrich Jiang; Michael I. Jordan (AAAI, 2021)

Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization. - Jelena Diakonikolas; Constantinos Daskalakis; Michael I. Jordan (AISTATS, 2021)

On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. - Tianyi Lin; Zeyu Zheng; Elynn Y. Chen; Marco Cuturi; Michael I. Jordan (AISTATS, 2021)

A Variational Inequality Approach to Bayesian Regression Games. - Wenshuo Guo; Michael I. Jordan; Tianyi Lin (CDC, 2021)

On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective. - Tyler Westenbroek; Max Simchowitz; Michael I. Jordan; S. Shankar Sastry (CDC, 2021)

Elastic Hyperparameter Tuning on the Cloud. - Lisa Dunlap; Kirthevasan Kandasamy; Ujval Misra; Richard Liaw; Michael I. Jordan; Ion Stoica; Joseph E. Gonzalez (SoCC, 2021)

Stochastic Approximation for Online Tensorial Independent Component Analysis. - Chris Junchi Li; Michael I. Jordan (COLT, 2021)

The Stereotyping Problem in Collaboratively Filtered Recommender Systems. - Wenshuo Guo; Karl Krauth; Michael I. Jordan; Nikhil Garg 001 (EAAMO, 2021)

Uncertainty Sets for Image Classifiers using Conformal Prediction. - Anastasios Nikolas Angelopoulos; Stephen Bates; Michael I. Jordan; Jitendra Malik (ICLR, 2021)

Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. - Esther Rolf; Theodora T. Worledge; Benjamin Recht; Michael I. Jordan (ICML, 2021)

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 001 (ICML, 2021)

Provable Meta-Learning of Linear Representations. - Nilesh Tripuraneni; Chi Jin 001; Michael I. Jordan (ICML, 2021)

The 4th Artificial Intelligence of Things (AIoT) Workshop. - Jian Zhang; Jian Tang 008; Yiran Chen 001; Jie Liu 001; Jieping Ye; Marilyn Wolf; Vijaykrishnan Narayanan; Mani Srivastava 001; Michael I. Jordan; Victor Bahl (KDD, 2021)

On the Theory of Reinforcement Learning with Once-per-Episode Feedback. - Niladri S. Chatterji; Aldo Pacchiano; Peter L. Bartlett; Michael I. Jordan (NeurIPS, 2021)

Learning in Multi-Stage Decentralized Matching Markets. - Xiaowu Dai; Michael I. Jordan (NeurIPS, 2021)

Robust Learning of Optimal Auctions. - Wenshuo Guo; Michael I. Jordan; Emmanouil Zampetakis (NeurIPS, 2021)

On Component Interactions in Two-Stage Recommender Systems. - Jiri Hron; Karl Krauth; Michael I. Jordan; Niki Kilbertus (NeurIPS, 2021)

Learning Equilibria in Matching Markets from Bandit Feedback. - Meena Jagadeesan; Alexander Wei 001; Yixin Wang 002; Michael I. Jordan; Jacob Steinhardt (NeurIPS, 2021)

Test-time Collective Prediction. - Celestine Mendler-Dünner; Wenshuo Guo; Stephen Bates; Michael I. Jordan (NeurIPS, 2021)

Tactical Optimism and Pessimism for Deep Reinforcement Learning. - Ted Moskovitz; Jack Parker-Holder; Aldo Pacchiano; Michael Arbel; Michael I. Jordan (NeurIPS, 2021)

Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic. - Yufeng Zhang 007; Siyu Chen 001; Zhuoran Yang; Michael I. Jordan; Zhaoran Wang 001 (NeurIPS, 2021)

Who Leads and Who Follows in Strategic Classification? - Tijana Zrnic; Eric Mazumdar; S. Shankar Sastry; Michael I. Jordan (NeurIPS, 2021)

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 (UAI, 2021)

A 1.5nJ/cls Unsupervised Online Learning Classifier for Seizure Detection. - Adelson Chua; Michael I. Jordan; Rikky Muller (VLSI Circuits, 2021)

Private Prediction Sets. - Anastasios N. Angelopoulos; Stephen Bates; Tijana Zrnic; Michael I. Jordan (CoRR, 2021)

Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors. - Xiaowu Dai; Michael I. Jordan (CoRR, 2021)

Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley. - Ani Adhikari; John DeNero; Michael I. Jordan (CoRR, 2021)

Multi-Source Causal Inference Using Control Variates. - Wenshuo Guo; Serena Lutong Wang; Peng Ding; Yixin Wang 002; Michael I. Jordan (CoRR, 2021)

Parallelizing Contextual Linear Bandits. - Jeffrey Chan; Aldo Pacchiano; Nilesh Tripuraneni; Yun S. Song; Peter L. Bartlett; Michael I. Jordan (CoRR, 2021)

PAC Best Arm Identification Under a Deadline. - Brijen Thananjeyan; Kirthevasan Kandasamy; Ion Stoica; Michael I. Jordan; Ken Goldberg; Joseph E. Gonzalez (CoRR, 2021)

Online Learning of Competitive Equilibria in Exchange Economies. - Wenshuo Guo; Kirthevasan Kandasamy; Joseph E. Gonzalez; Michael I. Jordan; Ion Stoica (CoRR, 2021)

Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning. - Koulik Khamaru; Eric Xia; Martin J. Wainwright; Michael I. Jordan (CoRR, 2021)

On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. - Chris Junchi Li; Yaodong Yu; Nicolas Loizou; Gauthier Gidel; Yi Ma 001; Nicolas Le Roux; Michael I. Jordan (CoRR, 2021)

Data Sharing Markets. - Mohammad Rasouli 001; Michael I. Jordan (CoRR, 2021)

Desiderata for Representation Learning: A Causal Perspective. - Yixin Wang 002; Michael I. Jordan (CoRR, 2021)

Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control. - Anastasios N. Angelopoulos; Stephen Bates; Emmanuel J. Candès; Michael I. Jordan; Lihua Lei (CoRR, 2021)

On the Self-Penalization Phenomenon in Feature Selection. - Michael I. Jordan; Keli Liu; Feng Ruan (CoRR, 2021)

Ranking and Tuning Pre-trained Models: A New Paradigm of Exploiting Model Hubs. - Kaichao You; Yong Liu; Jianmin Wang 001; Michael I. Jordan; Mingsheng Long (CoRR, 2021)

Cluster-and-Conquer: A Framework For Time-Series Forecasting. - Reese Pathak; Rajat Sen; Nikhil Rao 001; N. Benjamin Erichson; Michael I. Jordan; Inderjit S. Dhillon (CoRR, 2021)

ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning. - Xiao-Yang Liu; Zechu Li; Zhuoran Yang; Jiahao Zheng 002; Zhaoran Wang 001; Anwar Walid; Jian Guo; Michael I. Jordan (CoRR, 2021)

Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers? - Han Zhong 001; Zhuoran Yang; Zhaoran Wang 001; Michael I. Jordan (CoRR, 2021)

Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations. - Tatjana Chavdarova; Michael I. Jordan; Manolis Zampetakis (CoRR, 2021)

Polyak-Ruppert Averaged Q-Leaning is Statistically Efficient. - Xiang Li 050; Wenhao Yang; Zhihua Zhang 004; Michael I. Jordan (CoRR, 2021)

Distribution-free, Risk-controlling Prediction Sets. - Stephen Bates; Anastasios Angelopoulos; Lihua Lei; Jitendra Malik; Michael I. Jordan (J. ACM, 2021)

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (J. ACM, 2021)

Learning Strategies in Decentralized Matching Markets under Uncertain Preferences. - Xiaowu Dai; Michael I. Jordan (J. Mach. Learn. Res., 2021)

Bandit Learning in Decentralized Matching Markets. - Lydia T. Liu; Feng Ruan; Horia Mania; Michael I. Jordan (J. Mach. Learn. Res., 2021)

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. - Wenlong Mou; Yi-An Ma; Martin J. Wainwright; Peter L. Bartlett; Michael I. Jordan (J. Mach. Learn. Res., 2021)

Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives. - Michael Muehlebach; Michael I. Jordan (J. Mach. Learn. Res., 2021)

A Lyapunov Analysis of Accelerated Methods in Optimization. - Ashia C. Wilson; Ben Recht; Michael I. Jordan (J. Mach. Learn. Res., 2021)

Asynchronous Online Testing of Multiple Hypotheses. - Tijana Zrnic; Aaditya Ramdas; Michael I. Jordan (J. Mach. Learn. Res., 2021)

Generalized Momentum-Based Methods: A Hamiltonian Perspective. - Jelena Diakonikolas; Michael I. Jordan (SIAM J. Optim., 2021)

Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis. - Koulik Khamaru; Ashwin Pananjady; Feng Ruan; Martin J. Wainwright; Michael I. Jordan (SIAM J. Math. Data Sci., 2021)

Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. - Wenshuo Guo; Kirthevasan Kandasamy; Joseph Gonzalez 001; Michael I. Jordan; Ion Stoica (AISTATS, 2022)

On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms. - Nhat Ho; Tianyi Lin; Michael I. Jordan (AISTATS, 2022)

On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. - Chris Junchi Li; Yaodong Yu; Nicolas Loizou; Gauthier Gidel; Yi Ma 001; Nicolas Le Roux; Michael I. Jordan (AISTATS, 2022)

Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. - Yaodong Yu; Tianyi Lin; Eric V. Mazumdar; Michael I. Jordan (AISTATS, 2022)

Partial Identification with Noisy Covariates: A Robust Optimization Approach. - Wenshuo Guo; Mingzhang Yin; Yixin Wang 002; Michael I. Jordan (CLeaR, 2022)

Optimal Mean Estimation without a Variance. - Yeshwanth Cherapanamjeri; Nilesh Tripuraneni; Peter L. Bartlett; Michael I. Jordan (COLT, 2022)

ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. - Chris Junchi Li; Wenlong Mou; Martin J. Wainwright; Michael I. Jordan (COLT, 2022)

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 (ICML, 2022)

No-Regret Learning in Partially-Informed Auctions. - Wenshuo Guo; Michael I. Jordan; Ellen Vitercik (ICML, 2022)

Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. - Tianyi Lin; Aldo Pacchiano; Yaodong Yu; Michael I. Jordan (ICML, 2022)

Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy. - Zhihan Liu; Miao Lu; Zhaoran Wang 001; Michael I. Jordan; Zhuoran Yang (ICML, 2022)

Robust Estimation for Non-parametric Families via Generative Adversarial Networks. - Banghua Zhu; Jiantao Jiao; Michael I. Jordan (ISIT, 2022)

Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail and Beyond. - Zhiwei (Tony) Qin; Liangjie Hong; Rui Song 006; Hongtu Zhu; Mohammed Korayem; Haiyan Luo; Michael I. Jordan (KDD, 2022)

The 5th Artificial Intelligence of Things (AIoT) Workshop. - Jian Zhang; Jian Tang 008; Yiran Chen 001; Jie Liu 001; Jieping Ye; Marilyn Wolf; Vijaykrishnan Narayanan; Mani B. Srivastava; Michael I. Jordan; Victor Bahl (KDD, 2022)

Rank Diminishing in Deep Neural Networks. - Ruili Feng; Kecheng Zheng; Yukun Huang; Deli Zhao; Michael I. Jordan; Zheng-Jun Zha (NeurIPS, 2022)

Off-Policy Evaluation with Policy-Dependent Optimization Response. - Wenshuo Guo; Michael I. Jordan; Angela Zhou (NeurIPS, 2022)

On-Demand Sampling: Learning Optimally from Multiple Distributions. - Nika Haghtalab; Michael I. Jordan; Eric Zhao 003 (NeurIPS, 2022)

First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. - Michael I. Jordan; Tianyi Lin; Emmanouil V. Vlatakis-Gkaragkounis (NeurIPS, 2022)

Empirical Gateaux Derivatives for Causal Inference. - Michael I. Jordan; Yixin Wang 002; Angela Zhou (NeurIPS, 2022)

Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. - Chris Junchi Li; Dongruo Zhou; Quanquan Gu; Michael I. Jordan (NeurIPS, 2022)

Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization. - Tianyi Lin; Zeyu Zheng; Michael I. Jordan (NeurIPS, 2022)

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. - Yifei Min; Tianhao Wang 002; Ruitu Xu; Zhaoran Wang 001; Michael I. Jordan; Zhuoran Yang (NeurIPS, 2022)

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. - Yaodong Yu; Alexander Wei 001; Sai Praneeth Karimireddy; Yi Ma 001; Michael I. Jordan (NeurIPS, 2022)

Robust Calibration with Multi-domain Temperature Scaling. - Yaodong Yu; Stephen Bates; Yi Ma 001; Michael I. Jordan (NeurIPS, 2022)

Identifying Systematic Variation at the Single-Cell Level by Leveraging Low-Resolution Population-Level Data. - Elior Rahmani; Michael I. Jordan; Nir Yosef (RECOMB, 2022)

Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. - Jibang Wu; Zixuan Zhang; Zhe Feng 004; Zhaoran Wang 001; Zhuoran Yang; Michael I. Jordan; Haifeng Xu (EC, 2022)

Optimal variance-reduced stochastic approximation in Banach spaces. - Wenlong Mou; Koulik Khamaru; Martin J. Wainwright; Peter L. Bartlett; Michael I. Jordan (CoRR, 2022)

Online Active Learning with Dynamic Marginal Gain Thresholding. - Mariel A. Werner; Anastasios Angelopoulos; Stephen Bates; Michael I. Jordan (CoRR, 2022)

Reinforcement Learning with Heterogeneous Data: Estimation and Inference. - Elynn Y. Chen; Rui Song 006; Michael I. Jordan (CoRR, 2022)

Robust Estimation for Nonparametric Families via Generative Adversarial Networks. - Banghua Zhu; Jiantao Jiao; Michael I. Jordan (CoRR, 2022)

Conformal prediction for the design problem. - Clara Fannjiang; Stephen Bates; Anastasios N. Angelopoulos; Jennifer Listgarten; Michael I. Jordan (CoRR, 2022)

Transferred Q-learning. - Elynn Y. Chen; Michael I. Jordan; Sai Li (CoRR, 2022)

Improving Generalization via Uncertainty Driven Perturbations. - Matteo Pagliardini; Gilberto Manunza; Martin Jaggi; Michael I. Jordan; Tatjana Chavdarova (CoRR, 2022)

Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach. - Boxiang Lyu; Qinglin Meng; Shuang Qiu; Zhaoran Wang 001; Zhuoran Yang; Michael I. Jordan (CoRR, 2022)

Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents. - Alessandro Barp; Lancelot Da Costa; Guilherme França; Karl J. Friston; Mark A. Girolami; Michael I. Jordan; Grigorios A. Pavliotis (CoRR, 2022)

Perseus: A Simple High-Order Regularization Method for Variational Inequalities. - Tianyi Lin; Michael I. Jordan (CoRR, 2022)

Principal-Agent Hypothesis Testing. - Stephen Bates; Michael I. Jordan; Michael Sklar; Jake A. Soloff (CoRR, 2022)

The Sky Above The Clouds. - Sarah E. Chasins; Alvin Cheung; Natacha Crooks; Ali Ghodsi 002; Ken Goldberg; Joseph E. Gonzalez; Joseph M. Hellerstein; Michael I. Jordan; Anthony D. Joseph; Michael W. Mahoney; Aditya G. Parameswaran; David A. Patterson 001; et al. (CoRR, 2022)

Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees. - Banghua Zhu; Lun Wang 001; Qi Pang; Shuai Wang 011; Jiantao Jiao; Dawn Song; Michael I. Jordan (CoRR, 2022)

A Continuous-Time Perspective on Monotone Equation Problems. - Tianyi Lin; Michael I. Jordan (CoRR, 2022)

Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. - Simon S. Du; Gauthier Gidel; Michael I. Jordan; Chris Junchi Li (CoRR, 2022)

Solving Constrained Variational Inequalities via an Interior Point Method. - Tong Yang; Michael I. Jordan; Tatjana Chavdarova (CoRR, 2022)

NumS: Scalable Array Programming for the Cloud. - Melih Elibol; Vinamra Benara; Samyu Yagati; Lianmin Zheng; Alvin Cheung; Michael I. Jordan; Ion Stoica (CoRR, 2022)

Breaking Feedback Loops in Recommender Systems with Causal Inference. - Karl Krauth; Yixin Wang 002; Michael I. Jordan (CoRR, 2022)

Mechanisms that Incentivize Data Sharing in Federated Learning. - Sai Praneeth Karimireddy; Wenshuo Guo; Michael I. Jordan (CoRR, 2022)

Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata. - Tatjana Chavdarova; Ya-Ping Hsieh; Michael I. Jordan (CoRR, 2022)

Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. - Chris Junchi Li; Dongruo Zhou; Quanquan Gu; Michael I. Jordan (CoRR, 2022)

Valid Inference after Causal Discovery. - Paula Gradu; Tijana Zrnic; Yixin Wang 002; Michael I. Jordan (CoRR, 2022)

On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization. - Michael I. Jordan; Tianyi Lin; Manolis Zampetakis (CoRR, 2022)

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. - Zixiang Chen; Chris Junchi Li; Angela Yuan; Quanquan Gu; Michael I. Jordan (CoRR, 2022)

A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design. - Rui Ai 002; Boxiang Lyu; Zhaoran Wang 001; Zhuoran Yang; Michael I. Jordan (CoRR, 2022)

Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee. - Tianyi Lin; Panayotis Mertikopoulos; Michael I. Jordan (CoRR, 2022)

Revisiting the ACVI Method for Constrained Variational Inequalities. - Tatjana Chavdarova; Matteo Pagliardini; Tong Yang; Michael I. Jordan (CoRR, 2022)

Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization. - Chris Junchi Li; Angela Yuan; Gauthier Gidel; Michael I. Jordan (CoRR, 2022)

Incentive-Aware Recommender Systems in Two-Sided Markets. - Xiaowu Dai; Yuan Qi 001; Michael I. Jordan (CoRR, 2022)

Convergence Rates for Gaussian Mixtures of Experts. - Nhat Ho; Chiao-Yu Yang; Michael I. Jordan (J. Mach. Learn. Res., 2022)

On the Complexity of Approximating Multimarginal Optimal Transport. - Tianyi Lin; Nhat Ho; Marco Cuturi; Michael I. Jordan (J. Mach. Learn. Res., 2022)

On the Efficiency of Entropic Regularized Algorithms for Optimal Transport. - Tianyi Lin; Nhat Ho; Michael I. Jordan (J. Mach. Learn. Res., 2022)

Active Learning for Nonlinear System Identification with Guarantees. - Horia Mania; Michael I. Jordan; Benjamin Recht (J. Mach. Learn. Res., 2022)

On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems. - Michael Muehlebach; Michael I. Jordan (J. Mach. Learn. Res., 2022)

Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs. - Kaichao You; Yong Liu; Ziyang Zhang; Jianmin Wang 001; Michael I. Jordan; Mingsheng Long (J. Mach. Learn. Res., 2022)

SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier. - Adelson Chua; Michael I. Jordan; Rikky Muller (IEEE J. Solid State Circuits, 2022)

A control-theoretic perspective on optimal high-order optimization. - Tianyi Lin; Michael I. Jordan (Math. Program., 2022)

Understanding the acceleration phenomenon via high-resolution differential equations. - Bin Shi; Simon S. Du; Michael I. Jordan; Weijie J. Su (Math. Program., 2022)

KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail, and Beyond. - Zhiwei (Tony) Qin; Liangjie Hong; Rui Song 006; Hongtu Zhu; Mohammed Korayem; Haiyan Luo; Michael I. Jordan (SIGKDD Explor., 2022)

Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. - Samuel Horváth; Lihua Lei; Peter Richtárik; Michael I. Jordan (SIAM J. Math. Data Sci., 2022)

Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias. - Wenshuo Guo; Serena Lutong Wang; Peng Ding; Yixin Wang 002; Michael I. Jordan (Trans. Mach. Learn. Res., 2022)

Competition, Alignment, and Equilibria in Digital Marketplaces. - Meena Jagadeesan; Michael I. Jordan; Nika Haghtalab (AAAI, 2023)

A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. - Xiang Li 050; Wenhao Yang; Jiadong Liang; Zhihua Zhang 004; Michael I. Jordan (AISTATS, 2023)

Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning. - Ruitu Xu; Yifei Min; Tianhao Wang 002; Michael I. Jordan; Zhaoran Wang 001; Zhuoran Yang (AISTATS, 2023)

Byzantine-Robust Federated Learning with Optimal Statistical Rates. - Banghua Zhu; Lun Wang 001; Qi Pang; Shuai Wang 011; Jiantao Jiao; Dawn Song; Michael I. Jordan (AISTATS, 2023)

An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. - Aldo Pacchiano; Peter L. Bartlett; Michael I. Jordan (ALT, 2023)

Deterministic Nonsmooth Nonconvex Optimization. - Michael I. Jordan; Guy Kornowski; Tianyi Lin; Ohad Shamir; Manolis Zampetakis (COLT, 2023)

Recommendation Systems with Distribution-Free Reliability Guarantees. - Anastasios N. Angelopoulos; Karl Krauth; Stephen Bates; Yixin Wang 002; Michael I. Jordan (COPA, 2023)

Neural Dependencies Emerging from Learning Massive Categories. - Ruili Feng; Kecheng Zheng; Kai Zhu 004; Yujun Shen; Jian Zhao; Yukun Huang; Deli Zhao; Jingren Zhou 001; Michael I. Jordan; Zheng-Jun Zha (CVPR, 2023)

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. - Zixiang Chen; Chris Junchi Li; Huizhuo Yuan; Quanquan Gu; Michael I. Jordan (ICLR, 2023)

Modeling content creator incentives on algorithm-curated platforms. - Jiri Hron; Karl Krauth; Michael I. Jordan; Niki Kilbertus; Sarah Dean (ICLR, 2023)

Solving Constrained Variational Inequalities via a First-order Interior Point-based Method. - Tong Yang; Michael I. Jordan; Tatjana Chavdarova (ICLR, 2023)

Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. - Chris Junchi Li; Huizhuo Yuan; Gauthier Gidel; Quanquan Gu; Michael I. Jordan (ICML, 2023)

Federated Conformal Predictors for Distributed Uncertainty Quantification. - Charles Lu 001; Yaodong Yu; Sai Praneeth Karimireddy; Michael I. Jordan; Ramesh Raskar (ICML, 2023)

Online Learning in Stackelberg Games with an Omniscient Follower. - Geng Zhao; Banghua Zhu; Jiantao Jiao; Michael I. Jordan (ICML, 2023)

Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. - Banghua Zhu; Michael I. Jordan; Jiantao Jiao (ICML, 2023)

KDD-2023 Workshop on Decision Intelligence and Analytics for Online Marketplaces. - Zhiwei (Tony) Qin; Rui Song 006; Jieping Ye; Hongtu Zhu; Michael I. Jordan (KDD, 2023)

On Learning Necessary and Sufficient Causal Graphs. - Hengrui Cai; Yixin Wang 002; Michael I. Jordan; Rui Song 006 (NeurIPS, 2023)

Class-Conditional Conformal Prediction with Many Classes. - Tiffany Ding; Anastasios Angelopoulos; Stephen Bates; Michael I. Jordan; Ryan J. Tibshirani (NeurIPS, 2023)

A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning. - Nika Haghtalab; Michael I. Jordan; Eric Zhao 003 (NeurIPS, 2023)

Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition. - Meena Jagadeesan; Michael I. Jordan; Jacob Steinhardt; Nika Haghtalab (NeurIPS, 2023)

Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure. - Angela Yuan; Chris Junchi Li; Gauthier Gidel; Michael I. Jordan; Quanquan Gu; Simon S. Du (NeurIPS, 2023)

Towards Optimal Caching and Model Selection for Large Model Inference. - Banghua Zhu; Ying Sheng 007; Lianmin Zheng; Clark W. Barrett; Michael I. Jordan; Jiantao Jiao (NeurIPS, 2023)

Doubly-Robust Self-Training. - Banghua Zhu; Mingyu Ding; Philip L. Jacobson; Ming Wu; Wei Zhan; Michael I. Jordan; Jiantao Jiao (NeurIPS, 2023)

Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback. - Romil Bhardwaj; Kirthevasan Kandasamy; Asim Biswal; Wenshuo Guo; Benjamin Hindman; Joseph Gonzalez 001; Michael I. Jordan; Ion Stoica (OSDI, 2023)

The Sample Complexity of Online Contract Design. - Banghua Zhu; Stephen Bates; Zhuoran Yang; Yixin Wang 002; Jiantao Jiao; Michael I. Jordan (EC, 2023)

Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. - Chris Junchi Li; Michael I. Jordan (UAI, 2023)

Prediction-Powered Inference. - Anastasios N. Angelopoulos; Stephen Bates; Clara Fannjiang; Michael I. Jordan; Tijana Zrnic (CoRR, 2023)

Accelerated First-Order Optimization under Nonlinear Constraints. - Michael Muehlebach; Michael I. Jordan (CoRR, 2023)

A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning. - Nika Haghtalab; Michael I. Jordan; Eric Zhao 003 (CoRR, 2023)

Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning. - Ruitu Xu; Yifei Min; Tianhao Wang 002; Zhaoran Wang 001; Michael I. Jordan; Zhuoran Yang (CoRR, 2023)

Online Learning in a Creator Economy. - Banghua Zhu; Sai Praneeth Karimireddy; Jiantao Jiao; Michael I. Jordan (CoRR, 2023)

Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry. - Serena Wang 001; Stephen Bates; P. M. Aronow; Michael I. Jordan (CoRR, 2023)

On Optimal Caching and Model Multiplexing for Large Model Inference. - Banghua Zhu; Ying Sheng 007; Lianmin Zheng; Clark W. Barrett; Michael I. Jordan; Jiantao Jiao (CoRR, 2023)

Fine-Tuning Language Models with Advantage-Induced Policy Alignment. - Banghua Zhu; Hiteshi Sharma; Felipe Vieira Frujeri; Shi Dong; Chenguang Zhu; Michael I. Jordan; Jiantao Jiao (CoRR, 2023)

Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. - Baihe Huang; Sai Praneeth Karimireddy; Michael I. Jordan (CoRR, 2023)

Incentivizing High-Quality Content in Online Recommender Systems. - Xinyan Hu; Meena Jagadeesan; Michael I. Jordan; Jacob Steinhardt (CoRR, 2023)

Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds. - Yang Cai 001; Michael I. Jordan; Tianyi Lin; Argyris Oikonomou; Emmanouil V. Vlatakis-Gkaragkounis (CoRR, 2023)

Accelerating Inexact HyperGradient Descent for Bilevel Optimization. - Haikuo Yang; Luo Luo; Chris Junchi Li; Michael I. Jordan (CoRR, 2023)

Incentive-Theoretic Bayesian Inference for Collaborative Science. - Stephen Bates; Michael I. Jordan; Michael Sklar; Jake A. Soloff (CoRR, 2023)

Scaff-PD: Communication Efficient Fair and Robust Federated Learning. - Yaodong Yu; Sai Praneeth Karimireddy; Yi Ma 001; Michael I. Jordan (CoRR, 2023)

A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning. - Neha S. Wadia; Yatin Dandi; Michael I. Jordan (CoRR, 2023)

Towards Optimal Statistical Watermarking. - Baihe Huang; Banghua Zhu; Hanlin Zhu; Jason D. Lee; Jiantao Jiao; Michael I. Jordan (CoRR, 2023)

Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? - Han Zhong 001; Zhuoran Yang; Zhaoran Wang 001; Michael I. Jordan (J. Mach. Learn. Res., 2023)

First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. - Michael I. Jordan; Tianyi Lin; Manolis Zampetakis (J. Mach. Learn. Res., 2023)

VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback. - Kirthevasan Kandasamy; Joseph E. Gonzalez; Michael I. Jordan; Ion Stoica (J. Mach. Learn. Res., 2023)

On Learning Rates and Schrödinger Operators. - Bin Shi; Weijie Su 001; Michael I. Jordan (J. Mach. Learn. Res., 2023)

Instance-Dependent Confidence and Early Stopping for Reinforcement Learning. - Eric Xia; Koulik Khamaru; Martin J. Wainwright; Michael I. Jordan (J. Mach. Learn. Res., 2023)

Monotone Inclusions, Acceleration, and Closed-Loop Control. - Tianyi Lin; Michael I. Jordan (Math. Oper. Res., 2023)

Skilful nowcasting of extreme precipitation with NowcastNet. - Yuchen Zhang 004; Mingsheng Long; Kaiyuan Chen; Lanxiang Xing; Ronghua Jin; Michael I. Jordan; Jianmin Wang 001 (Nat., 2023)

Provably Personalized and Robust Federated Learning. - Mariel A. Werner; Lie He; Michael I. Jordan; Martin Jaggi; Sai Praneeth Karimireddy (Trans. Mach. Learn. Res., 2023)

Learning Equilibria in Matching Markets with Bandit Feedback. - Meena Jagadeesan; Alexander Wei 001; Yixin Wang 002; Michael I. Jordan; Jacob Steinhardt (J. ACM, 2023)

Provably Efficient Reinforcement Learning with Linear Function Approximation. - Chi Jin 001; Zhuoran Yang; Zhaoran Wang 001; Michael I. Jordan (Math. Oper. Res., 2023)

Delegating Data Collection in Decentralized Machine Learning. - Nivasini Ananthakrishnan; Stephen Bates; Michael I. Jordan; Nika Haghtalab (AISTATS, 2024)

Classifier Calibration with ROC-Regularized Isotonic Regression. - Eugene Berta; Francis R. Bach; Michael I. Jordan (AISTATS, 2024)

A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. - Tianyi Lin; Marco Cuturi; Michael I. Jordan (AISTATS, 2024)

On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry. - Serena Wang 001; Stephen Bates; P. M. Aronow; Michael I. Jordan (AISTATS, 2024)

Privacy Can Arise Endogenously in an Economic System with Learning Agents. - Nivasini Ananthakrishnan; Tiffany Ding; Mariel A. Werner; Sai Praneeth Karimireddy; Michael I. Jordan (FORC, 2024)

A Primal-Dual Approach to Solving Variational Inequalities with General Constraints. - Tatjana Chavdarova; Tong Yang; Matteo Pagliardini; Michael I. Jordan (ICLR, 2024)

Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. - Tianyu Guo 004; Sai Praneeth Karimireddy; Michael I. Jordan (ICML, 2024)

Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. - Wei-Lin Chiang; Lianmin Zheng; Ying Sheng 007; Anastasios Nikolas Angelopoulos; Tianle Li; Dacheng Li; Banghua Zhu; Hao Zhang 025; Michael I. Jordan; Joseph E. Gonzalez; Ion Stoica (ICML, 2024)

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 (ICML, 2024)

Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF. - Banghua Zhu; Michael I. Jordan; Jiantao Jiao (ICML, 2024)

Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions. - Jordan Lekeufack; Anastasios N. Angelopoulos; Andrea Bajcsy; Michael I. Jordan; Jitendra Malik (ICRA, 2024)

Data Acquisition via Experimental Design for Data Markets. - Charles Lu 001; Baihe Huang; Sai Praneeth Karimireddy; Praneeth Vepakomma; Michael I. Jordan; Ramesh Raskar (NeurIPS, 2024)

Fair Allocation in Dynamic Mechanism Design. - Alireza Fallah 001; Michael I. Jordan; Annie Ulichney (NeurIPS, 2024)

Fairness-Aware Meta-Learning via Nash Bargaining. - Yi Zeng 005; Xuelin Yang; Li Chen; Cristian Canton Ferrer; Ming Jin 002; Michael I. Jordan; Ruoxi Jia 001 (NeurIPS, 2024)

Unravelling in Collaborative Learning. - Aymeric Capitaine; Etienne Boursier; Antoine Scheid; Eric Moulines; Michael I. Jordan; El-Mahdi El-Mhamdi; Alain Durmus (NeurIPS, 2024)

Dimension-free Private Mean Estimation for Anisotropic Distributions. - Yuval Dagan; Michael I. Jordan; Xuelin Yang; Lydia Zakynthinou; Nikita Zhivotovskiy (NeurIPS, 2024)

Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality. - Antoine Scheid; Aymeric Capitaine; Etienne Boursier; Eric Moulines; Michael I. Jordan; Alain Durmus (NeurIPS, 2024)

Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. - Hanlin Zhu; Baihe Huang; Shaolun Zhang; Michael I. Jordan; Jiantao Jiao; Yuandong Tian; Stuart J. Russell (NeurIPS, 2024)

Contract Design With Safety Inspections. - Alireza Fallah 001; Michael I. Jordan (EC, 2024)

The Limits of Price Discrimination Under Privacy Constraints. - Alireza Fallah 001; Michael I. Jordan; Ali Makhdoumi; Azarakhsh Malekian (CoRR, 2024)

On Three-Layer Data Markets. - Alireza Fallah 001; Michael I. Jordan; Ali Makhdoumi; Azarakhsh Malekian (CoRR, 2024)

Information Elicitation in Agency Games. - Serena Wang 001; Michael I. Jordan; Katrina Ligett; R. Preston McAfee (CoRR, 2024)

AutoEval Done Right: Using Synthetic Data for Model Evaluation. - Pierre Boyeau; Anastasios N. Angelopoulos; Nir Yosef; Jitendra Malik; Michael I. Jordan (CoRR, 2024)

Data Acquisition via Experimental Design for Decentralized Data Markets. - Charles Lu 001; Baihe Huang; Sai Praneeth Karimireddy; Praneeth Vepakomma; Michael I. Jordan; Ramesh Raskar (CoRR, 2024)

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction. - Drew T. Nguyen; Reese Pathak; Anastasios N. Angelopoulos; Stephen Bates; Michael I. Jordan (CoRR, 2024)

Reduced-Rank Multi-objective Policy Learning and Optimization. - Ezinne Nwankwo; Michael I. Jordan; Angela Zhou (CoRR, 2024)

Defection-Free Collaboration between Competitors in a Learning System. - Mariel A. Werner; Sai Praneeth Karimireddy; Michael I. Jordan (CoRR, 2024)

Mitigating Externalities while Learning: an Online Version of the Coase Theorem. - Antoine Scheid; Aymeric Capitaine; Etienne Boursier; Eric Moulines; Michael I. Jordan; Alain Durmus (CoRR, 2024)

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization. - Tianyi Lin; Chi Jin 001; Michael I. Jordan (CoRR, 2024)

Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry. - Meena Jagadeesan; Michael I. Jordan; Jacob Steinhardt (CoRR, 2024)

Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs. - Tianyu Guo 004; Druv Pai; Yu Bai 017; Jiantao Jiao; Michael I. Jordan; Song Mei (CoRR, 2024)

Optimal Design for Reward Modeling in RLHF. - Antoine Scheid; Etienne Boursier; Alain Durmus; Michael I. Jordan; Pierre Ménard; Eric Moulines; Michal Valko (CoRR, 2024)

Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity. - Eric Zhao 003; Tatjana Chavdarova; Michael I. Jordan (CoRR, 2024)

An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints. - Jordan Lekeufack; Michael I. Jordan (CoRR, 2024)

A Diffusion Process Perspective on Posterior Contraction Rates for Parameters. - Wenlong Mou; Nhat Ho; Martin J. Wainwright; Peter L. Bartlett; Michael I. Jordan (SIAM J. Math. Data Sci., 2024)

Incentive-Aware Recommender Systems in Two-Sided Markets. - Xiaowu Dai; Wenlu Xu; Yuan Qi 001; Michael I. Jordan (Trans. Recomm. Syst., 2024)

Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy. - Maryam Aliakbarpour; Syomantak Chaudhuri; Thomas A. Courtade; Alireza Fallah 001; Michael I. Jordan (AISTATS, 2025)

Automatically Adaptive Conformal Risk Control. - Vincent Blot; Anastasios Nikolas Angelopoulos; Michael I. Jordan; Nicolas J.-B. Brunel (AISTATS, 2025)

Relying on the Metrics of Evaluated Agents. - Serena Wang 001; Michael I. Jordan; Katrina Ligett; R. Preston McAfee (WWW, 2025)

Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback. - Michael I. Jordan; Tianyi Lin; Zhengyuan Zhou (Oper. Res., 2025)

A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games. - Francisca Vasconcelos; Emmanouil-Vasileios Vlatakis-Gkaragkounis; Panayotis Mertikopoulos; Georgios Piliouras; Michael I. Jordan (Quantum, 2025)

Gradient Equilibrium in Online Learning: Theory and Applications. - Anastasios N. Angelopoulos; Michael I. Jordan; Ryan J. Tibshirani (CoRR, 2025)

Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores. - Jivat Neet Kaur; Michael I. Jordan; Ahmed Alaa (CoRR, 2025)

The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective. - Michael Muehlebach; Zhiyu He; Michael I. Jordan (CoRR, 2025)

Prediction-Aware Learning in Multi-Agent Systems. - Aymeric Capitaine; Etienne Boursier; Eric Moulines; Michael I. Jordan; Alain Durmus (CoRR, 2025)

Rethinking Early Stopping: Refine, Then Calibrate. - Eugene Berta; David Holzmüller; Michael I. Jordan; Francis R. Bach (CoRR, 2025)

Learning Contracts in Hierarchical Multi-Agent Systems. - Antoine Scheid; Etienne Boursier; Alain Durmus; Eric Moulines; Michael I. Jordan (CoRR, 2025)

Perseus: a simple and optimal high-order method for variational inequalities. - Tianyi Lin; Michael I. Jordan (Math. Program., 2025)

Statistical Collusion by Collectives on Learning Platforms. - Etienne Gauthier; Francis R. Bach; Michael I. Jordan (CoRR, 2025)

How Do LLMs Perform Two-Hop Reasoning in Context? - Tianyu Guo 004; Hanlin Zhu; Ruiqi Zhang; Jiantao Jiao; Song Mei; Michael I. Jordan; Stuart Russell 001 (CoRR, 2025)

Conformal Prediction under Lévy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations. - Liviu Aolaritei; Michael I. Jordan; Youssef M. Marzouk; Zheyu Oliver Wang; Julie Zhu (CoRR, 2025)

An Overview of Large Language Models for Statisticians. - Wenlong Ji; Weizhe Yuan; Emily J. Getzen; Kyunghyun Cho; Michael I. Jordan; Song Mei; Jason Aaron Edward Weston; Weijie J. Su; Jing Xu; Linjun Zhang (CoRR, 2025)

The Role of the Marketplace Operator in Inducing Competition. - Tiffany Ding; Dominique Perrault-Joncas; Orit Ronen; Michael I. Jordan; Dirk Bergemann; Dean P. Foster; Omer Gottesman (CoRR, 2025)

E-Values Expand the Scope of Conformal Prediction. - Etienne Gauthier; Francis R. Bach; Michael I. Jordan (CoRR, 2025)

Minimum Volume Conformal Sets for Multivariate Regression. - Sacha Braun; Liviu Aolaritei; Michael I. Jordan; Francis R. Bach (CoRR, 2025)

Stochastic Optimization with Optimal Importance Sampling. - Liviu Aolaritei; Bart P. G. Van Parys; Henry Lam; Michael I. Jordan (CoRR, 2025)

Understanding In-context Learning of Addition via Activation Subspaces. - Xinyan Hu; Kayo Yin; Michael I. Jordan; Jacob Steinhardt; Lijie Chen 001 (CoRR, 2025)

Online Decision-Focused Learning. - Aymeric Capitaine; Maxime Haddouche; Eric Moulines; Michael I. Jordan; Etienne Boursier; Alain Durmus (CoRR, 2025)

Backward Conformal Prediction. - Etienne Gauthier; Francis R. Bach; Michael I. Jordan (CoRR, 2025)

IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis. - Hanyu Li; Haoyu Liu; Tingyu Zhu; Tianyu Guo 004; Zeyu Zheng; Xiaotie Deng; Michael I. Jordan (CoRR, 2025)

Sample Complexity and Representation Ability of Test-time Scaling Paradigms. - Baihe Huang; Shanda Li; Tianhao Wu 002; Yiming Yang; Ameet Talwalkar; Kannan Ramchandran; Michael I. Jordan; Jiantao Jiao (CoRR, 2025)

Revisiting mean estimation over ℓp balls: Is the MLE optimal? - Liviu Aolaritei; Michael I. Jordan; Reese Pathak; Annie Ulichney (CoRR, 2025)

Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers. - Yixiao Huang 004; Hanlin Zhu; Tianyu Guo 004; Jiantao Jiao; Somayeh Sojoudi; Michael I. Jordan; Stuart Russell 001; Song Mei (CoRR, 2025)

Valid Selection among Conformal Sets. - Mahmoud Hegazy; Liviu Aolaritei; Michael I. Jordan; Aymeric Dieuleveut (CoRR, 2025)

A Collectivist, Economic Perspective on AI. - Michael I. Jordan (CoRR, 2025)

A General Framework for Estimating Preferences Using Response Time Data. - Federico Echenique; Alireza Fallah 001; Michael I. Jordan (CoRR, 2025)

Multivariate Conformal Prediction via Conformalized Gaussian Scoring. - Sacha Braun; Eugene Berta; Michael I. Jordan; Francis R. Bach (CoRR, 2025)