Frameworks and Results in Distributionally Robust Optimization

The concepts of risk aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. statistical learning community has also witnessed a rapid theoretical applied growth by relying on these concepts. A modeling framework, called distributionally (DRO), recently received significant attention in both operations research communities. This paper surveys main contributions to DRO, relationships with function regularization. Various approaches model distributional ambiguity their calibrations are discussed. describes solution techniques used solve resulting problems.