74 citations · 170 across the 14 of their papers we have counts for
5 papers · 1 filter
Adaptive Bregman Proximal Stochastic Gradient with a Stabilized Barzilai--Borwein Step Size
Chenhan Jin, Shengze Xu, Binghui Xie +4
Bregman proximal stochastic gradient (BPSG) methods bring variance-reduced composite optimization to objectives whose geometry is poorly captured by Euclidean smoothness. Their per…
Follower Agnostic Methods for Stackelberg Games
Chinmay Maheshwari, James Cheng, S. Shankar Sasty +2
In this paper, we present an efficient algorithm to solve online Stackelberg games, featuring multiple followers, in a follower-agnostic manner. Unlike previous works, our approach…
An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms
Binghui Xie, Chenhan Jin, Kaiwen Zhou +2
Stochastic variance reduced methods have shown strong performance in solving finite-sum problems. However, these methods usually require the users to manually tune the step-size, w…
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization
Kaiwen Zhou, Anthony Man-Cho So, James Cheng
We show that stochastic acceleration can be achieved under the perturbed iterate framework (Mania et al., 2017) in asynchronous lock-free optimization, which leads to the optimal i…
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums
Kaiwen Zhou, Lai Tian, Anthony Man-Cho So +1
In convex optimization, the problem of finding near-stationary points has not been adequately studied yet, unlike other optimality measures such as the function value. Even in the…