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

Behavioral systems theory decouples the behavior of a system from its representation. A key
result is that, under a persistency of excitation condition, the image of a Hankel matrix …

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

We study distributionally robust optimization (DRO) with Sinkhorn distance--a variant of
Wasserstein distance based on entropic regularization. We derive convex programming …

We review distributionally robust optimization (DRO), a principled approach for constructing
statistical estimators that hedge against the impact of deviations in the expected loss …

Optimal transport (OT) is a popular measure to compare probability distributions. However,
OT suffers a few drawbacks such as (i) a high complexity for computation,(ii) indefiniteness …

We establish an equivalence between a family of adversarial training problems for non-
parametric binary classification and a family of regularized risk minimization problems where …

Wasserstein distributionally robust estimators have emerged as powerful models for
prediction and decision-making under uncertainty. These estimators provide attractive …

In the past few years, there has been considerable interest in two prominent approaches for
Distributionally Robust Optimization (DRO): Divergence-based and Wasserstein-based …

We propose a data-driven portfolio selection model that integrates side information,
conditional estimation and robustness using the framework of distributionally robust …