141 citations · 213 across the 13 of their papers we have counts for
6 papers · 1 filter
Symmetric (Optimistic) Natural Policy Gradient for Multi-agent Learning with Parameter Convergence
Sarath Pattathil, Kaiqing Zhang, Asuman Ozdaglar
Multi-agent interactions are increasingly important in the context of reinforcement learning, and the theoretical foundations of policy gradient methods have attracted surging rese…
An Optimal Multistage Stochastic Gradient Method for Minimax Problems
Alireza Fallah, Asuman Ozdaglar, Sarath Pattathil
In this paper, we study the minimax optimization problem in the smooth and strongly convex-strongly concave setting when we have access to noisy estimates of gradients. In particul…
A Decentralized Proximal Point-type Method for Saddle Point Problems
Weijie Liu, Aryan Mokhtari, Asuman Ozdaglar +3
In this paper, we focus on solving a class of constrained non-convex non-concave saddle point problems in a decentralized manner by a group of nodes in a network. Specifically, we…
Convergence Rate of for Optimistic Gradient and Extra-gradient Methods in Smooth Convex-Concave Saddle Point Problems
Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil
We study the iteration complexity of the optimistic gradient descent-ascent (OGDA) method and the extra-gradient (EG) method for finding a saddle point of a convex-concave unconstr…
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach
Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil
In this paper we consider solving saddle point problems using two variants of Gradient Descent-Ascent algorithms, Extra-gradient (EG) and Optimistic Gradient Descent Ascent (OGDA)…
Concentration bounds for two time scale stochastic approximation
Vivek S. Borkar, Sarath Pattathil
Viewing a two time scale stochastic approximation scheme as a noisy discretization of a singularly perturbed differential equation, we obtain a concentration bound for its iterates…