← Authors

Michael I. Jordan

178 papers · 2000–2025 · 12 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (53) 🐝 Cross-Pollinator (13) πŸƒ Academic Marathon (25) 🌍 Conference Polyglot (12) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (9) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌟 Keyword Trendsetter Combo (14) 🏠 Conference Loyalist (96) πŸ‘‘ Domain Dominant (128) πŸ† Keyword Champion (3) πŸ”¬ Deep Specialist (15) 🌱 Topic Pioneer 🀝 Dynamic Duo (32) πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (340) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer ⚑ Prolific Year (12) πŸ”₯ Unstoppable (20) ❓ The Questioner (3) πŸ’Ž Century Club (178)

Conferences

NIPS (96) JMLR (42) AISTATS (12) COLT (9) ICML (7) AAAI (3) CVPR (2) ICLR (2) NAACL (2) ICCV (1) OSDI (1) UAI (1)

Top co-authors

Michael I Jordan (32) Martin J. Wainwright (16) Chi Jin (11) Tianyi Lin (10) Jianmin Wang (9) Mingsheng Long (9) Zhihua Zhang (8) Benjamin Recht (7) Yuchen Zhang (7) nhαΊ­t Hα»“ (7)

Research topics

Privacy (2) Differential Privacy (1) Probability (1)

Keywords

bayesian inference (16) markov chain monte carlo (14) convergence rate (12) sample complexity (10) convex optimization (9) stochastic optimization (9) kernel methods (8) reinforcement learning (8) variational inference (8) regret bound (7) nonconvex optimization (6) domain adaptation (6) neural network (6) feature selection (6) optimal transport (6) non-convex optimization (6) game theory (6) transfer learning (5) model selection (5) statistical learning (5)

Papers

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 SchrΓΆdinger 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