Asuman Ozdaglar
26 papers · 2013–2026 · 6 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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(12)
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(2)
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Research topics
Machine Learning
(23)
Optimization & Theory
(19)
Artificial Intelligence
(12)
Mathematics & Optimization
(12)
Optimization
(12)
Learning Types
(8)
Core AI
(6)
Reinforcement Learning
(5)
Learning Paradigms Β· Artificial Intelligence
(5)
Methods
(5)
Bayesian & Probabilistic Β· Machine Learning
(3)
Deep Learning
(2)
Bayesian & Probabilistic Β· Artificial Intelligence
(2)
Core Methods
(2)
Learning Paradigms Β· Machine Learning
(2)
Keywords
convex optimization
(6)
minimax optimization
(5)
zero-sum game
(5)
non-convex optimization
(4)
generalization bound
(4)
saddle point
(4)
markov game
(4)
model-agnostic meta-learning
(3)
nash equilibrium
(3)
generative adversarial network
(2)
federated learning
(2)
stochastic gradient descent
(2)
extragradient method
(2)
algorithmic stability
(2)
convergence rate
(2)
distributed learning
(2)
stochastic gradient
(2)
stochastic optimization
(2)
gradient descent
(2)
gradient descent ascent
(2)
Papers
Learning Decision-Sufficient Representations for Linear Optimization
COLT 2026
Regret Minimization with Adaptive Opponents in Repeated Games
COLT 2026
EM for Mixture of Linear Regression with Clustered Data
AISTATS 2024
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
NeurIPS 2023
Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
NeurIPS 2023
Symmetric (Optimistic) Natural Policy Gradient for Multi-Agent Learning with Parameter Convergence
AISTATS 2023
Time-Reversed Dissipation Induces Duality Between Minimizing Gradient Norm and Function Value
NeurIPS 2023
Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms
NeurIPS 2022
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
JMLR 2022
What is a Good Metric to Study Generalization of Minimax Learners?
NeurIPS 2022
A Wasserstein Minimax Framework for Mixed Linear Regression
ICML 2021
Decentralized Q-learning in Zero-sum Markov Games
NeurIPS 2021
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks
NeurIPS 2021
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
NeurIPS 2021
Train simultaneously, generalize better: Stability of gradient-based minimax learners
ICML 2021
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach
AISTATS 2020
Bayesian Learning with Adaptive Load Allocation Strategies
L4DC 2020
Do GANs always have Nash equilibria?
ICML 2020
Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems
COLT 2020
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
AISTATS 2020
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
NeurIPS 2020
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
NeurIPS 2019
Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods
AISTATS 2019
Escaping Saddle Points in Constrained Optimization
NeurIPS 2018
When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent
NeurIPS 2017
Computing the Stationary Distribution Locally
NeurIPS 2013