no code implementations • 27 Jan 2024 • Yiling Xie, Xiaoming Huo
Alternatively, a two-step procedure is proposed -- adaptive adversarial training, which could further improve the performance of adversarial training under $\ell_\infty$-perturbation.
no code implementations • 27 Mar 2023 • Yiling Xie, Xiaoming Huo
We propose an adjusted Wasserstein distributionally robust estimator -- based on a nonlinear transformation of the Wasserstein distributionally robust (WDRO) estimator in statistical learning.
no code implementations • 23 Jan 2023 • Yiling Luo, Yiling Xie, Xiaoming Huo
To compare, we prove that the computational complexity of the Stochastic Sinkhorn algorithm is $\widetilde{{O}}({n^2}/{\epsilon^2})$, which is slower than our accelerated primal-dual stochastic mirror algorithm.
no code implementations • 29 Oct 2022 • Yiling Xie, Yiling Luo, Xiaoming Huo
Computing the empirical Wasserstein distance in the independence test requires solving this special type of OT problem, where $m=n^2$.
1 code implementation • 2 Mar 2022 • Yiling Xie, Yiling Luo, Xiaoming Huo
A primal-dual accelerated stochastic gradient descent with variance reduction algorithm (PDASGD) is proposed to solve linear-constrained optimization problems.