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

The multiple criteria sorting decision is a critical area of research in decision science.
However, accurately capturing the decision maker's cognition for each criterion using …

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 …

In this paper we present novel data-driven optimization models for Support Vector Machines
(SVM), with the aim of linearly separating two sets of points that have non-disjoint convex …

Wasserstein distributionally robust optimization (\textsf {WDRO}) is a popular model to
enhance the robustness of machine learning with ambiguous data. However, the complexity …

The Halpern iteration for solving monotone inclusion problems has gained increasing
interests in recent years due to its simple form and appealing convergence properties. In this …

Wasserstein distributionally robust optimization has recently emerged as a powerful
framework for robust estimation, enjoying good out-of-sample performance guarantees, well …

Incremental gradient methods and incremental proximal methods are a fundamental class of
optimization algorithms used for solving finite sum problems, broadly studied in the …

Distributionally robust optimization has emerged as an attractive way to train robust machine
learning models, capturing data uncertainty and distribution shifts. Recent statistical …