3 papers
math.OC2025
Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods
Yasong Feng, Yifan Jiang, Tianyu Wang +1
This work provides a novel convergence analysis for stochastic optimization in terms of stopping times, addressing the practical reality that algorithms are often terminated adapti…
math.PR2025
A transfer principle for computing the adapted Wasserstein distance between stochastic processes
Yifan Jiang, Fang Rui Lim
We propose a transfer principle to study the adapted 2-Wasserstein distance between stochastic processes. First, we obtain an explicit formula for the distance between real-valued…
cs.LG2025
Wasserstein distributional adversarial training for deep neural networks
Xingjian Bai, Guangyi He, Yifan Jiang +1
Design of adversarial attacks for deep neural networks, as well as methods of adversarial training against them, are subject of intense research. In this paper, we propose methods…