In this paper, we survey the primary research on the theory and applications of
distributionally robust optimization (DRO). We start with reviewing the modeling power and …

Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …

Wasserstein distributionally robust optimization (DRO) aims to find robust and generalizable
solutions by hedging against data perturbations in Wasserstein distance. Despite its recent …

Abstract Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is
studied in various fields such as engineering, computer science, economics, and …

This monograph develops a comprehensive statistical learning framework that is robust to
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …

Federated learning can effectively ensure data security and improve the problem of data
islanding. However, the performance of federated learning-based schemes could be better …

We study the implicit regularization of mini-batch stochastic gradient descent, when applied
to the fundamental problem of least squares regression. We leverage a continuous-time …

Distributionally robust optimization (DRO) is an effective approach for data-driven decision-
making in the presence of uncertainty. Geometric uncertainty due to~ sampling or localized …

This paper provides an overview of results and concepts in minimax robust hypothesis
testing for two and multiple hypotheses. It starts with an introduction to the subject …

We consider statistical methods that invoke a min-max distributionally robust formulation to
extract good out-of-sample performance in data-driven optimization and learning problems …