75 citations · 897 across the 91 of their papers we have counts for
7 papers · 1 filter
Conditional Gradient Methods
Gábor Braun, Alejandro Carderera, Cyrille W. Combettes +4
The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithm…
Submodular + Concave
Siddharth Mitra, Moran Feldman, Amin Karbasi
It has been well established that first order optimization methods can converge to the maximal objective value of concave functions and provide constant factor approximation guaran…
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…
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…