46 citations · 178 across the 31 of their papers we have counts for
6 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…
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…
Optimal Algorithms for Submodular Maximization with Distributed Constraints
Alexander Robey, Arman Adibi, Brent Schlotfeldt +2
We consider a class of discrete optimization problems that aim to maximize a submodular objective function subject to a distributed partition matroid constraint. More precisely, we…
Stochastic Conditional Gradient++
Hamed Hassani, Amin Karbasi, Aryan Mokhtari +1
In this paper, we consider the general non-oblivious stochastic optimization where the underlying stochasticity may change during the optimization procedure and depends on the poin…
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
This paper considers stochastic optimization problems for a large class of objective functions, including convex and continuous submodular. Stochastic proximal gradient methods hav…
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
In this paper, we showcase the interplay between discrete and continuous optimization in network-structured settings. We propose the first fully decentralized optimization method f…