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 area of maritime transportation optimization has recently begun to achieve increasing
success at solving large scale models, and industry is steadily adopting operations research …

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

Deriving decisions from data typically involves a sequential process with two components,
forecasting and optimization. Forecasting models learn by minimizing a loss function that …

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

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 …

In quantitative finance, prediction models are traditionally optimized independently from their
use in the asset allocation decision-making process. We address this limitation and present …

Many real-world optimization problems involve uncertain parameters with probability
distributions that can be estimated using contextual feature information. In contrast to the …