Asuman Ozdaglar
24 papers · 2013–2024 · 6 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+11 more ↓ Show less ↑
π§ Keyword Pioneer π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (12) π Conference Polyglot (6)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Interdisciplinary Bridge
π¬
Deep Specialist
(12)
ποΈ
Keyword Collector
(117)
β‘
Prolific Year
(5)
π
Conference Pioneer
π
Century Club
(24)
π₯
Unstoppable
(8)
π
Trend Setter
β
The Questioner
(2)
Conferences
NIPS (13)
AISTATS (5)
ICML (3)
COLT (1)
JMLR (1)
L4DC (1)
Top co-authors
Keywords
convex optimization
(5)
minimax optimization
(5)
zero-sum game
(5)
saddle point
(4)
nonconvex optimization
(3)
generalization bound
(3)
model-agnostic meta-learning
(3)
nash equilibrium
(3)
markov game
(3)
stochastic gradient descent
(2)
generative adversarial network
(2)
federated learning
(2)
gradient descent ascent
(2)
algorithmic stability
(2)
convergence rate
(2)
distributed learning
(2)
stochastic gradient
(2)
stochastic optimization
(2)
gradient descent
(2)
proximal point method
(2)
Papers
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
NIPS 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
NIPS 2023
Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
NIPS 2023
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
JMLR 2022
Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms
NIPS 2022
What is a Good Metric to Study Generalization of Minimax Learners?
NIPS 2022
Train simultaneously, generalize better: Stability of gradient-based minimax learners
ICML 2021
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
NIPS 2021
A Wasserstein Minimax Framework for Mixed Linear Regression
ICML 2021
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks
NIPS 2021
Decentralized Q-learning in Zero-sum Markov Games
NIPS 2021
Bayesian Learning with Adaptive Load Allocation Strategies
L4DC 2020
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach
NIPS 2020
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
AISTATS 2020
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach
AISTATS 2020
Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems
COLT 2020
Do GANs always have Nash equilibria?
ICML 2020
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
NIPS 2019
Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods
AISTATS 2019
Escaping Saddle Points in Constrained Optimization
NIPS 2018
When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent
NIPS 2017
Computing the Stationary Distribution Locally
NIPS 2013