15 citations · 66 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
Efficient Projection-Free Online Methods with Stochastic Recursive Gradient
Jiahao Xie, Zebang Shen, Chao Zhang +2
This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems. Existing projection-free methods either achieve suboptimal regret bounds…
Aggregated Gradient Langevin Dynamics
Chao Zhang, Jiahao Xie, Zebang Shen +3
In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling. We investigate the nonasymptotic conver…
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…
A Stochastic Trust Region Method for Non-convex Minimization
Zebang Shen, Pan Zhou, Cong Fang +1
We target the problem of finding a local minimum in non-convex finite-sum minimization. Towards this goal, we first prove that the trust region method with inexact gradient and Hes…
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…