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 …

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 …

We consider data-driven approaches that integrate a machine learning prediction model
within distributionally robust optimization (DRO) given limited joint observations of uncertain …

We propose a Bayesian framework for assessing the relative strengths of data-driven
ambiguity sets in distributionally robust optimization (DRO) when the underlying distribution …

We develop a modular and tractable framework for solving an adaptive distributionally
robust linear optimization problem, where we minimize the worst-case expected cost over an …

Wasserstein distributionally robust optimization (DRO) is an approach to optimization under
uncertainty in which the decision maker hedges against a set of probability distributions …

A key step in solving minimax distributionally robust optimization (DRO) problems is to
reformulate the inner maximization wrt probability measure as a semiinfinite programming …

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

We consider optimal transport-based distributionally robust optimization (DRO) problems
with locally strongly convex transport cost functions and affine decision rules. Under …