5 papers
Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
Yike Zhao, Onno Eberhard, Malek Khammassi +2
The family of linear recurrent neural networks has shown strong performance as recurrent memory units in partially observable reinforcement learning. We provide a theoretical justi…
Stability and Generalization of Adversarial Diffusion Training
Hesam Hosseini, Ying Cao, Ali H. Sayed
Algorithmic stability is an established tool for analyzing generalization. While adversarial training enhances model robustness, it often suffers from robust overfitting and an enl…
On the Escaping Efficiency of Distributed Adversarial Training Algorithms
Ying Cao, Kun Yuan, Ali H. Sayed
Adversarial training has been widely studied in recent years due to its role in improving model robustness against adversarial attacks. This paper focuses on comparing different di…
On the Trade-off between Flatness and Optimization in Distributed Learning
Ying Cao, Zhaoxian Wu, Kun Yuan +1
This paper proposes a theoretical framework to evaluate and compare the performance of stochastic gradient algorithms for distributed learning in relation to their behavior around…
Diffusion Learning with Partial Agent Participation and Local Updates
Elsa Rizk, Kun Yuan, Ali H. Sayed
Diffusion learning is a framework that endows edge devices with advanced intelligence. By processing and analyzing data locally and allowing each agent to communicate with its imme…