Stability and Sharper Risk Bounds with Convergence Rate $\tilde{O}(1/n^2)$
Machine Learning
2025-10-31 v2 Machine Learning
Abstract
Prior work (Klochkov Zhivotovskiy, 2021) establishes at most excess risk bounds via algorithmic stability for strongly-convex learners with high probability. We show that under the similar common assumptions -- - Polyak-Lojasiewicz condition, smoothness, and Lipschitz continous for losses -- - rates of are at most achievable. To our knowledge, our analysis also provides the tightest high-probability bounds for gradient-based generalization gaps in nonconvex settings.
Cite
@article{arxiv.2410.09766,
title = {Stability and Sharper Risk Bounds with Convergence Rate $\tilde{O}(1/n^2)$},
author = {Bowei Zhu and Shaojie Li and Mingyang Yi and Yong Liu},
journal= {arXiv preprint arXiv:2410.09766},
year = {2025}
}