Online Stochastic Gradient Methods Under Sub-Weibull Noise and the Polyak-Lojasiewicz Condition

被引：0

作者：

University of Colorado, Department of Applied Mathematics, Boulder, United States ^{[1
]}

不详 ^{[2
]}

机构：

来源：

Proc IEEE Conf Decis Control | 2022年 / 3499-3506期

关键词：

Compendex;

D O I：

61st IEEE Conference on Decision and Control, CDC 2022

中图分类号：

学科分类号：

摘要：

Gradient methods

引用

共 20 条

[11] Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Lojasiewicz Condition
Fan, Chen
Thrampoulidis, Christos
Schmidt, Mark
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES: RESEARCH TRACK, ECML PKDD 2023, PT IV, 2023, 14172 : 301 - 315
[12] NON-ERGODIC LINEAR CONVERGENCE PROPERTY OF THE DELAYED GRADIENT DESCENT UNDER THE STRONGLY CONVEXITY AND THE POLYAK-LOJASIEWICZ CONDITION
Choi, Hyung Jun
Choi, Woocheol
Seok, Jinmyoung
arXiv, 2023,
[13] Non-ergodic linear convergence property of the delayed gradient descent under the strongly convexity and the Polyak-Lojasiewicz condition
Choi, Hyung Jun
Choi, Woocheol
Seok, Jinmyoung
ANALYSIS AND APPLICATIONS, 2024, 22 (06) : 1023 - 1051
[14] Gradient-Type Method for Optimization Problems with Polyak-Lojasiewicz Condition: Relative Inexactness in Gradient and Adaptive Parameters Setting
Puchinin, Sergei M.
Stonyakin, Fedor S.
arXiv, 2023,
[15] High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise
Madden, Liam
Dall'Anese, Emiliano
Becker, Stephen
JOURNAL OF MACHINE LEARNING RESEARCH, 2024, 25 : 1 - 36
[16] Convergence rates for the heavy-ball continuous dynamics for non-convex optimization, under Polyak-Lojasiewicz condition
Apidopoulos, Vassilis
Ginatta, Nicolo
Villa, Silvia
JOURNAL OF GLOBAL OPTIMIZATION, 2022, 84 (03) : 563 - 589
[17] Distributionally Time-Varying Online Stochastic Optimization under Polyak- Lojasiewicz Condition with Application in Conditional Value-at-Risk Statistical Learning
Pun, Yuen-Man
Farokhi, Farhad
Shames, Iman
arXiv, 2023,
[18] Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks
An, Jing
Lu, Jianfeng
arXiv, 2023,
[19] Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness
Scaman, Kevin
Malherbe, Cédric
Dos Santos, Ludovic
Proceedings of Machine Learning Research, 2022, 162 : 19310 - 19327
[20] Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness
Scaman, Kevin
Malherbe, Cedric
Dos Santos, Ludovic
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, 2022, : 19310 - 19327

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