1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2024
The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization
Matan Schliserman, Uri Sherman, Tomer Koren
We study the generalization performance of gradient methods in the fundamental stochastic convex optimization setting, focusing on its dimension dependence. First, for full-batch g…
cs.LG2023★ 1 cited
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation
Uri Sherman, Tomer Koren, Yishay Mansour
We study reinforcement learning with linear function approximation and adversarially changing cost functions, a setup that has mostly been considered under simplifying assumptions…