Michael I. Jordan

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