17 citations · 44 across the 14 of their papers we have counts for
14 papers
Generative Neural Networks for Sinkhorn Distributionally Robust Hypothesis Testing
Fenglin Zhang, Teyan Liu, Jie Wang
This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust detector against least-favorable distributions in Sinkhorn discrepancy-…
Contextual Distributionally Robust Optimization with Causal and Continuous Structure
Fenglin Zhang, Jie Wang
We propose a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution, and we develop a…
Iterative Sampling Methods for Sinkhorn Distributionally Robust Optimization
Jie Wang
Distributionally robust optimization (DRO) has emerged as a powerful paradigm for reliable decision-making under uncertainty. This paper focuses on DRO with ambiguity sets defined…
A Dual Riemannian Augmented Lagrangian Method for Low-Rank SDPs with Unit Diagonal
Jie Wang, Liangbing Hu, Bican Xia
We propose ManiDSDP, a dual Riemannian augmented Lagrangian method for solving low-rank semidefinite programs in the dual form whose positive semidefinite variable has unit diagona…
Regularization for Adversarial Robust Learning
Jie Wang, Rui Gao, Yao Xie
Despite the growing prevalence of artificial neural networks in real-world applications, their vulnerability to adversarial attacks remains a significant concern, which motivates u…
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances
Jie Wang, March Boedihardjo, Yao Xie
Optimal transport has been very successful for various machine learning tasks; however, it is known to suffer from the curse of dimensionality. Hence, dimensionality reduction is d…
Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets
Jie Wang, Rui Gao, Yao Xie
We present a new framework to address the non-convex robust hypothesis testing problem, wherein the goal is to seek the optimal detector that minimizes the maximum of worst-case ty…
Contextual Stochastic Bilevel Optimization
Yifan Hu, Jie Wang, Yao Xie +2
We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on…
Variable Selection for Kernel Two-Sample Tests
Jie Wang, Santanu S. Dey, Yao Xie
We consider the variable selection problem for two-sample tests, aiming to select the most informative variables to determine whether two collections of samples follow the same dis…
A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets
Jie Wang, Yao Xie
Hypothesis testing for small-sample scenarios is a practically important problem. In this paper, we investigate the robust hypothesis testing problem in a data-driven manner, where…
Sinkhorn Distributionally Robust Optimization
Jie Wang, Rui Gao, Yao Xie
We study distributionally robust optimization with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization. We derive a convex programming dual refo…
Two-sample Test with Kernel Projected Wasserstein Distance
Jie Wang, Rui Gao, Yao Xie
We develop a kernel projected Wasserstein distance for the two-sample test, an essential building block in statistics and machine learning: given two sets of samples, to determine…
Reliable Off-policy Evaluation for Reinforcement Learning
Jie Wang, Rui Gao, Hongyuan Zha
In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy using logged trajectory data generated from a different b…
Two-sample Test using Projected Wasserstein Distance
Jie Wang, Rui Gao, Yao Xie
We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they…