Michael I. Jordan

A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design JMLR 2026 Blackwell Approachability and Gradient Equilibrium are Equivalent COLT 2026 Calibeating Made Simple COLT 2026 AutoEval Done Right: Using Synthetic Data for Model Evaluation ICML 2025 Gradient Equilibrium in Online Learning: Theory and Applications JMLR 2025 Instability, Computational Efficiency and Statistical Accuracy JMLR 2025 Prediction-Aware Learning in Multi-Agent Systems ICML 2025 Statistical Collusion by Collectives on Learning Platforms ICML 2025 Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization JMLR 2025 Data Acquisition via Experimental Design for Data Markets NeurIPS 2024 Desiderata for Representation Learning: A Causal Perspective JMLR 2024 Dimension-free Private Mean Estimation for Anisotropic Distributions NeurIPS 2024 Fair Allocation in Dynamic Mechanism Design NeurIPS 2024 Fairness-Aware Meta-Learning via Nash Bargaining NeurIPS 2024 Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach JMLR 2024 Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality NeurIPS 2024 Unravelling in Collaborative Learning NeurIPS 2024 A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning AISTATS 2023 A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning NeurIPS 2023 Byzantine-Robust Federated Learning with Optimal Statistical Rates AISTATS 2023 Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? JMLR 2023 Class-Conditional Conformal Prediction with Many Classes NeurIPS 2023 Competition, Alignment, and Equilibria in Digital Marketplaces AAAI 2023 Doubly-Robust Self-Training NeurIPS 2023 Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning AISTATS 2023 First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems JMLR 2023 Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition NeurIPS 2023 Instance-Dependent Confidence and Early Stopping for Reinforcement Learning JMLR 2023 On Learning Necessary and Sufficient Causal Graphs NeurIPS 2023 On Learning Rates and Schrödinger Operators JMLR 2023 Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure NeurIPS 2023 Towards Optimal Caching and Model Selection for Large Model Inference NeurIPS 2023 Active Learning for Nonlinear System Identification with Guarantees JMLR 2022 Convergence Rates for Gaussian Mixtures of Experts JMLR 2022 Empirical Gateaux Derivatives for Causal Inference NeurIPS 2022 First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces NeurIPS 2022 Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization NeurIPS 2022 Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium NeurIPS 2022 Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets NeurIPS 2022 Off-Policy Evaluation with Policy-Dependent Optimization Response NeurIPS 2022 On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems JMLR 2022 On-Demand Sampling: Learning Optimally from Multiple Distributions NeurIPS 2022 On the Complexity of Approximating Multimarginal Optimal Transport JMLR 2022 On the Efficiency of Entropic Regularized Algorithms for Optimal Transport JMLR 2022 Rank Diminishing in Deep Neural Networks NeurIPS 2022 Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs JMLR 2022 Robust Calibration with Multi-domain Temperature Scaling NeurIPS 2022 TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels NeurIPS 2022 A Lyapunov Analysis of Accelerated Methods in Optimization JMLR 2021 Asynchronous Online Testing of Multiple Hypotheses JMLR 2021 Bandit Learning in Decentralized Matching Markets JMLR 2021 Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization AISTATS 2021 High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm JMLR 2021 Learning Equilibria in Matching Markets from Bandit Feedback NeurIPS 2021 Learning from eXtreme Bandit Feedback AAAI 2021 Learning in Multi-Stage Decentralized Matching Markets NeurIPS 2021 Learning Strategies in Decentralized Matching Markets under Uncertain Preferences JMLR 2021 On Component Interactions in Two-Stage Recommender Systems NeurIPS 2021 On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification AISTATS 2021 On the Theory of Reinforcement Learning with Once-per-Episode Feedback NeurIPS 2021 Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives JMLR 2021 Robust Learning of Optimal Auctions NeurIPS 2021 Robustness Guarantees for Mode Estimation with an Application to Bandits AAAI 2021 Tactical Optimism and Pessimism for Deep Reinforcement Learning NeurIPS 2021 Test-time Collective Prediction NeurIPS 2021 Variational refinement for importance sampling using the forward Kullback-Leibler divergence UAI 2021 Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic NeurIPS 2021 Who Leads and Who Follows in Strategic Classification? NeurIPS 2021 Decision-Making with Auto-Encoding Variational Bayes NeurIPS 2020 Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm NeurIPS 2020 Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data JMLR 2020 Langevin Monte Carlo without smoothness AISTATS 2020 Near-Optimal Algorithms for Minimax Optimization COLT 2020 On the Theory of Transfer Learning: The Importance of Task Diversity NeurIPS 2020 Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information AISTATS 2020 Projection Robust Wasserstein Distance and Riemannian Optimization NeurIPS 2020 Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations NeurIPS 2020 Robust Optimization for Fairness with Noisy Protected Groups NeurIPS 2020 Transferable Calibration with Lower Bias and Variance in Domain Adaptation NeurIPS 2020 Variance Reduction With Sparse Gradients ICLR 2020 Acceleration via Symplectic Discretization of High-Resolution Differential Equations NeurIPS 2019 L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data ICLR 2019 Transferable Normalization: Towards Improving Transferability of Deep Neural Networks NeurIPS 2019 Universal Domain Adaptation CVPR 2019 Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent COLT 2018 Averaging Stochastic Gradient Descent on Riemannian Manifolds COLT 2018 CoCoA: A General Framework for Communication-Efficient Distributed Optimization JMLR 2018 Conditional Adversarial Domain Adaptation NeurIPS 2018 Covariances, Robustness, and Variational Bayes JMLR 2018 Detection limits in the high-dimensional spiked rectangular model COLT 2018 Generalized Zero-Shot Learning with Deep Calibration Network NeurIPS 2018 Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation NeurIPS 2018 Information Constraints on Auto-Encoding Variational Bayes NeurIPS 2018 Is Q-Learning Provably Efficient? NeurIPS 2018 Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification COLT 2018 On the Local Minima of the Empirical Risk NeurIPS 2018 Partial Transfer Learning With Selective Adversarial Networks CVPR 2018 Ray: A Distributed Framework for Emerging AI Applications OSDI 2018 Saturating Splines and Feature Selection JMLR 2018 Stochastic Cubic Regularization for Fast Nonconvex Optimization NeurIPS 2018 Theoretical guarantees for EM under misspecified Gaussian mixture models NeurIPS 2018 Underdamped Langevin MCMC: A non-asymptotic analysis COLT 2018 Breaking Locality Accelerates Block Gauss-Seidel ICML 2017 Deep Transfer Learning with Joint Adaptation Networks ICML 2017 Fast Black-box Variational Inference through Stochastic Trust-Region Optimization NeurIPS 2017 Gradient Descent Can Take Exponential Time to Escape Saddle Points NeurIPS 2017 How to Escape Saddle Points Efficiently ICML 2017 Kernel Feature Selection via Conditional Covariance Minimization NeurIPS 2017 Non-convex Finite-Sum Optimization Via SCSG Methods NeurIPS 2017 Online control of the false discovery rate with decaying memory NeurIPS 2017 On the Learnability of Fully-Connected Neural Networks AISTATS 2017 Asymptotic behavior of \ell_p-based Laplacian regularization in semi-supervised learning COLT 2016 Cyclades: Conflict-free Asynchronous Machine Learning NeurIPS 2016 Gradient Descent Only Converges to Minimizers COLT 2016 L1-regularized Neural Networks are Improperly Learnable in Polynomial Time ICML 2016 Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences NeurIPS 2016 Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing JMLR 2016 Unsupervised Domain Adaptation with Residual Transfer Networks NeurIPS 2016 Distributed Matrix Completion and Robust Factorization JMLR 2015 Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes NeurIPS 2015 On the Accuracy of Self-Normalized Log-Linear Models NeurIPS 2015 Parallel Correlation Clustering on Big Graphs NeurIPS 2015 Variational Consensus Monte Carlo NeurIPS 2015 Communication-Efficient Distributed Dual Coordinate Ascent NeurIPS 2014 Lower Bounds on the Performance of Polynomial-time Algorithms for Sparse Linear Regression COLT 2014 On the Convergence Rate of Decomposable Submodular Function Minimization NeurIPS 2014 Parallel Double Greedy Submodular Maximization NeurIPS 2014 Particle Gibbs with Ancestor Sampling JMLR 2014 Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing NeurIPS 2014 A Comparative Framework for Preconditioned Lasso Algorithms NeurIPS 2013 Distributed Low-Rank Subspace Segmentation ICCV 2013 Estimation, Optimization, and Parallelism when Data is Sparse NeurIPS 2013 Information-theoretic lower bounds for distributed statistical estimation with communication constraints NeurIPS 2013 Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation NeurIPS 2013 Optimistic Concurrency Control for Distributed Unsupervised Learning NeurIPS 2013 Streaming Variational Bayes NeurIPS 2013 Ancestor Sampling for Particle Gibbs NeurIPS 2012 Coherence Functions with Applications in Large-Margin Classification Methods JMLR 2012 EP-GIG Priors and Applications in Bayesian Sparse Learning JMLR 2012 Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods NeurIPS 2012 Privacy Aware Learning NeurIPS 2012 Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models NeurIPS 2012 Bayesian Bias Mitigation for Crowdsourcing NeurIPS 2011 Bayesian Generalized Kernel Mixed Models JMLR 2011 Dimensionality Reduction for Spectral Clustering AISTATS 2011 Divide-and-Conquer Matrix Factorization NeurIPS 2011 Bayesian Generalized Kernel Models AISTATS 2010 Heavy-Tailed Process Priors for Selective Shrinkage NeurIPS 2010 Inference and Learning in Networks of Queues AISTATS 2010 Matrix-Variate Dirichlet Process Mixture Models AISTATS 2010 Random Conic Pursuit for Semidefinite Programming NeurIPS 2010 Regularized Discriminant Analysis, Ridge Regression and Beyond JMLR 2010 Tree-Structured Stick Breaking for Hierarchical Data NeurIPS 2010 Type-Based MCMC NAACL 2010 Unsupervised Kernel Dimension Reduction NeurIPS 2010 Variational Inference over Combinatorial Spaces NeurIPS 2010 Asymptotically Optimal Regularization in Smooth Parametric Models NeurIPS 2009 Nonparametric Latent Feature Models for Link Prediction NeurIPS 2009 Sharing Features among Dynamical Systems with Beta Processes NeurIPS 2009 DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification NeurIPS 2008 Efficient Inference in Phylogenetic InDel Trees NeurIPS 2008 High-dimensional support union recovery in multivariate regression NeurIPS 2008 Nonparametric Bayesian Learning of Switching Linear Dynamical Systems NeurIPS 2008 Posterior Consistency of the Silverman g-prior in Bayesian Model Choice NeurIPS 2008 Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes NeurIPS 2008 Spectral Clustering with Perturbed Data NeurIPS 2008 Agreement-Based Learning NeurIPS 2007 Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization NeurIPS 2007 Feature Selection Methods for Improving Protein Structure Prediction with Rosetta NeurIPS 2007 In-Network PCA and Anomaly Detection NeurIPS 2006 Learning Spectral Clustering, With Application To Speech Separation JMLR 2006 Structured Prediction, Dual Extragradient and Bregman Projections JMLR 2006 Word Alignment via Quadratic Assignment NAACL 2006 Latent Variable Models for Dimensionality Reduction AISTATS 2005 Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces JMLR 2004 Learning the Kernel Matrix with Semidefinite Programming JMLR 2004 Beyond Independent Components: Trees and Clusters JMLR 2003 Latent Dirichlet Allocation JMLR 2003 Matching Words and Pictures JMLR 2003 A Robust Minimax Approach to Classification JMLR 2002 Kernel Independent Component Analysis JMLR 2002 Learning with Mixtures of Trees JMLR 2000 Hierarchical Beta Processes and the Indian Buffet Process AISTATS 1999