Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods

13 August 2023

Papers citing "Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods"

9 / 9 papers shown

Title
Some Optimizers are More Equal: Understanding the Role of Optimizers in Group Fairness Mojtaba Kolahdouzi Hatice Gunes Ali Etemad 23 0 0 21 Apr 2025
A Mirror Descent Perspective of Smoothed Sign Descent Shuyang Wang Diego Klabjan 28 0 0 18 Oct 2024
SignSGD with Federated Voting Chanho Park H. Vincent Poor Namyoon Lee FedML 27 1 0 25 Mar 2024
On the Duality Between Sharpness-Aware Minimization and Adversarial Training Yihao Zhang Hangzhou He Jingyu Zhu Huanran Chen Yifei Wang Zeming Wei AAML 26 9 0 23 Feb 2024
Improving Adversarial Transferability via Model Alignment A. Ma Amir-massoud Farahmand Yangchen Pan Philip H. S. Torr Jindong Gu AAML 21 5 0 30 Nov 2023
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Hanxun Huang Yisen Wang S. Erfani Quanquan Gu James Bailey Xingjun Ma AAML TPM 44 100 0 07 Oct 2021
Improving Hierarchical Adversarial Robustness of Deep Neural Networks A. Ma Aladin Virmaux Kevin Scaman Juwei Lu AAML 13 5 0 17 Feb 2021
Secure and Robust Machine Learning for Healthcare: A Survey A. Qayyum Junaid Qadir Muhammad Bilal Ala I. Al-Fuqaha AAML OOD 31 368 0 21 Jan 2020
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 119 1,190 0 16 Aug 2016