Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

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

Contextual optimization, also known as predict-then-optimize or prescriptive analytics,
considers an optimization problem with the presence of covariates (context or side …

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 …

We study optimization for data-driven decision making when we have observations of the
uncertain parameters within an optimization model together with concurrent observations of …

In this paper, we study a novel approach for data-driven decision-making under uncertainty
in the presence of contextual information. Specifically, we solve this problem from a …

We study policy optimization for the feature-based newsvendor, which seeks an end-to-end
policy that renders an explicit mapping from features to ordering decisions. Most existing …

We consider stochastic programs conditional on some covariate information, where the only
knowledge of the possible relationship between the uncertain parameters and the …

Data-driven decision-making has garnered growing interest as a result of the increasing
availability of data in recent years. With that growth many opportunities and challenges have …

The field of Contextual Optimization (CO) integrates machine learning and optimization to
solve decision making problems under uncertainty. Recently, a risk sensitive variant of CO …