4 papers
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…
Multi-Agent Adversarial Training Using Diffusion Learning
Ying Cao, Elsa Rizk, Stefan Vlaski +1
This work focuses on adversarial learning over graphs. We propose a general adversarial training framework for multi-agent systems using diffusion learning. We analyze the converge…