96 citations · 286 across the 19 of their papers we have counts for
5 papers · 1 filter
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…
Robust Hypothesis Testing with Wasserstein Uncertainty Sets
Liyan Xie, Rui Gao, Yao Xie
We consider a data-driven robust hypothesis test where the optimal test will minimize the worst-case performance regarding distributions that are close to the empirical distributio…
Hierarchical Non-Stationary Temporal Gaussian Processes With -Regularization
Zheng Zhao, Rui Gao, Simo Särkkä
This paper is concerned with regularized extensions of hierarchical non-stationary temporal Gaussian processes (NSGPs) in which the parameters (e.g., length-scale) are modeled as G…
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…
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
Hao Wang, Rui Gao, Flavio P. Calmon
Machine learning models trained by different optimization algorithms under different data distributions can exhibit distinct generalization behaviors. In this paper, we analyze the…