We consider statistical methods that invoke a min-max distributionally robust formulation to
extract good out-of-sample performance in data-driven optimization and learning problems …

A typical data-driven stochastic program seeks the best decision that minimizes the sum of a
deterministic cost function and an expected recourse function under a given distribution …

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

Wasserstein balls, which contain all probability measures within a pre-specified Wasserstein
distance to a reference measure, have recently enjoyed wide popularity in the …

We focus on robust estimation of the unobserved state of a discrete-time stochastic system
with linear dynamics. A standard analysis of this estimation problem assumes a baseline …

Algorithmic recourse aims to recommend an informative feedback to overturn an
unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a …

Least squares estimators, when trained on few target domain samples, may predict poorly.
Supervised domain adaptation aims to improve the predictive accuracy by exploiting …

We consider decision-making problems with contextual information, in which the reward
function involves uncertain parameters that can be predicted using covariates. To quantify …

A fundamental problem arising in many areas of machine learning is the evaluation of the
likelihood of a given observation under different nominal distributions. Frequently, these …

We build a Bayesian contextual classification model using an optimistic score ratio for robust
binary classification when there is limited information on the class-conditional, or contextual …