Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints

19 July 2021

Papers citing "Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints"

9 / 9 papers shown

Title
Stability and Sharper Risk Bounds with Convergence Rate $O(1/n^2)$ Bowei Zhu Shaojie Li Yong Liu 16 0 0 13 Oct 2024
Towards Sharper Risk Bounds for Minimax Problems Bowei Zhu Shaojie Li Yong Liu 36 0 0 11 Oct 2024
Convex SGD: Generalization Without Early Stopping Julien Hendrickx A. Olshevsky MLT LRM 25 1 0 08 Jan 2024
Towards Understanding the Generalization of Graph Neural Networks Huayi Tang Y. Liu GNN AI4CE 32 29 0 14 May 2023
Sharper Utility Bounds for Differentially Private Models Yilin Kang Yong Liu Jian Li Weiping Wang FedML 23 3 0 22 Apr 2022
Learning Rates for Nonconvex Pairwise Learning Shaojie Li Yong Liu 25 2 0 09 Nov 2021
Data Heterogeneity Differential Privacy: From Theory to Algorithm Yilin Kang Jian Li Yong Liu Weiping Wang 16 1 0 20 Feb 2020
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 130 1,198 0 16 Aug 2016
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 99 570 0 08 Dec 2012