The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …

The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

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

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 …

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

We consider a reinforcement learning setting in which the deployment environment is
different from the training environment. Applying a robust Markov decision processes …

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

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 …

We study a distributionally robust mean square error estimation problem over a nonconvex
Wasserstein ambiguity set containing only normal distributions. We show that the optimal …

Policy learning using historical observational data is an important problem that has found
widespread applications. However, existing literature rests on the crucial assumption that …