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Michael Jordan

84 papers · 2007–2025 · 14 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (27) 🌈 Renaissance Researcher (5) 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (10) 🌍 Conference Polyglot (14) πŸƒ Academic Marathon (18) 🏠 Conference Loyalist (20) πŸ† Keyword Champion (3) πŸ† Grand Slam πŸ”¬ Deep Specialist (20) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (118) ⚑ Prolific Year (9) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter ❓ The Questioner πŸ’Ž Century Club (84) πŸ”₯ Unstoppable (11)

Conferences

ICML (42) AISTATS (20) ICLR (5) COLT (4) AAAI (3) ACL (2) ALT (1) CLEAR (1) CONLL (1) CVPR (1) EMNLP (1) IJCNLP (1) NIPS (1) OSDI (1)

Top co-authors

Tianyi Lin (8) Aldo Pacchiano (6) Peter Bartlett (6) Martin Wainwright (5) Percy Liang (5) Wenshuo Guo (5) Jiantao Jiao (5) Dan Klein (5) Yaodong Yu (5) nhαΊ­t Hα»“ (5)

Research topics

Applications (2) Statistics (2) Core AI (1)

Keywords

minimax optimization (5) online learning (5) variance reduction (5) multi-armed bandit (5) stochastic gradient descent (5) neural network (4) convergence rate (4) stochastic gradient (4) domain adaptation (4) regret bound (3) optimization algorithm (3) reinforcement learning (3) transfer learning (3) resource allocation (3) sample complexity (3) wasserstein distance (3) optimal transport (3) gradient descent (3) feature learning (2) game theory (2)

Papers

Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy AISTATS 2025
Automatically Adaptive Conformal Risk Control AISTATS 2025
On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry AISTATS 2024
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference ICML 2024
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics NIPS 2024
A Primal-Dual Approach to Solving Variational Inequalities with General Constraints ICLR 2024
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF ICML 2024
Incentivized Learning in Principal-Agent Bandit Games ICML 2024
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis ICML 2024
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport AISTATS 2024
Delegating Data Collection in Decentralized Machine Learning AISTATS 2024
Classifier Calibration with ROC-Regularized Isotonic Regression AISTATS 2024
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning ICLR 2023
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method ICLR 2023
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit ALT 2023
Modeling content creator incentives on algorithm-curated platforms ICLR 2023
Deterministic Nonsmooth Nonconvex Optimization COLT 2023
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons ICML 2023
Online Learning in Stackelberg Games with an Omniscient Follower ICML 2023
Federated Conformal Predictors for Distributed Uncertainty Quantification ICML 2023
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization ICML 2023
Neural Dependencies Emerging From Learning Massive Categories CVPR 2023
Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback OSDI 2023
Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy ICML 2022
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms AISTATS 2022
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization AISTATS 2022
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback AISTATS 2022
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging AISTATS 2022
Partial Identification with Noisy Covariates: A Robust Optimization Approach CLEAR 2022
Optimal Mean Estimation without a Variance COLT 2022
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm COLT 2022
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging ICML 2022
No-Regret Learning in Partially-Informed Auctions ICML 2022
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback ICML 2022
Provable Meta-Learning of Linear Representations ICML 2021
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism ICML 2021
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data ICML 2021
Stochastic Approximation for Online Tensorial Independent Component Analysis COLT 2021
Uncertainty Sets for Image Classifiers using Conformal Prediction ICLR 2021
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML 2020
Accelerated Message Passing for Entropy-Regularized MAP Inference ICML 2020
LS-Tree: Model Interpretation When the Data Are Linguistic AAAI 2020
Cost-Effective Incentive Allocation via Structured Counterfactual Inference AAAI 2020
Learning to Score Behaviors for Guided Policy Optimization ICML 2020
Continuous-time Lower Bounds for Gradient-based Algorithms ICML 2020
On Approximate Thompson Sampling with Langevin Algorithms ICML 2020
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games ICML 2020
ML-LOO: Detecting Adversarial Examples with Feature Attribution AAAI 2020
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference AISTATS 2020
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems ICML 2020
Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter AISTATS 2020
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models AISTATS 2020
Competing Bandits in Matching Markets AISTATS 2020
The Power of Batching in Multiple Hypothesis Testing AISTATS 2020
Stochastic Gradient and Langevin Processes ICML 2020
A Dynamical Systems Perspective on Nesterov Acceleration ICML 2019
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms ICML 2019
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers ICML 2019
Rao-Blackwellized Stochastic Gradients for Discrete Distributions ICML 2019
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation ICML 2019
Bridging Theory and Algorithm for Domain Adaptation ICML 2019
Theoretically Principled Trade-off between Robustness and Accuracy ICML 2019
Probabilistic Multilevel Clustering via Composite Transportation Distance AISTATS 2019
A Swiss Army Infinitesimal Jackknife AISTATS 2019
RLlib: Abstractions for Distributed Reinforcement Learning ICML 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation ICML 2018
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo ICML 2018
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate ICML 2018
Less than a Single Pass: Stochastically Controlled Stochastic Gradient AISTATS 2017
A Linearly-Convergent Stochastic L-BFGS Algorithm AISTATS 2016
A Kernelized Stein Discrepancy for Goodness-of-fit Tests ICML 2016
A General Analysis of the Convergence of ADMM ICML 2015
Adding vs. Averaging in Distributed Primal-Dual Optimization ICML 2015
Learning Transferable Features with Deep Adaptation Networks ICML 2015
Trust Region Policy Optimization ICML 2015
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds ICML 2015
Efficient Ranking from Pairwise Comparisons ICML 2013
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes ICML 2013
Stick-Breaking Beta Processes and the Poisson Process AISTATS 2012
Learning Dependency-Based Compositional Semantics ACL 2011
Learning Semantic Correspondences with Less Supervision ACL 2009
Learning Semantic Correspondences with Less Supervision IJCNLP 2009
The Infinite PCFG Using Hierarchical Dirichlet Processes CONLL 2007
The Infinite PCFG Using Hierarchical Dirichlet Processes EMNLP 2007