2 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
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: An Interpretable and Tractable Approach
Fenglin Zhang, Jie Wang
In this paper, we introduce a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution…
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…
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…
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…