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…