146 citations · 328 across the 17 of their papers we have counts for
21 papers · 1 filter
Minimax Optimization: The Case of Convex-Submodular
Arman Adibi, Aryan Mokhtari, Hamed Hassani
Minimax optimization has been central in addressing various applications in machine learning, game theory, and control theory. Prior literature has thus far mainly focused on study…
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach
Qiujiang Jin, Aryan Mokhtari
In this paper, we study the application of quasi-Newton methods for solving empirical risk minimization (ERM) problems defined over a large dataset. Traditional deterministic and s…
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…
One Sample Stochastic Frank-Wolfe
Mingrui Zhang, Zebang Shen, Aryan Mokhtari +2
One of the beauties of the projected gradient descent method lies in its rather simple mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its wi…
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate
Saeed Soori, Konstantin Mischenko, Aryan Mokhtari +2
In this paper, we consider distributed algorithms for solving the empirical risk minimization problem under the master/worker communication model. We develop a distributed asynchro…
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…