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cs.LG2024
Complexity of Vector-valued Prediction: From Linear Models to Stochastic Convex Optimization
Matan Schliserman, Tomer Koren
We study the problem of learning vector-valued linear predictors: these are prediction rules parameterized by a matrix that maps an -dimensional feature vector to a -dimensio…
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
Tight Risk Bounds for Gradient Descent on Separable Data
Matan Schliserman, Tomer Koren
We study the generalization properties of unregularized gradient methods applied to separable linear classification -- a setting that has received considerable attention since the…