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

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

We consider stochastic programs where the distribution of the uncertain parameters is only
observable through a finite training dataset. Using the Wasserstein metric, we construct a …

We combine ideas from machine learning (ML) and operations research and management
science (OR/MS) in developing a framework, along with specific methods, for using data to …

This monograph develops a comprehensive statistical learning framework that is robust to
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …

We present two methods, based on regression in reproducing kernel Hilbert spaces, for
solving an optimization problem with uncertain parameters for which we have historical data …

We study contextual stochastic optimization problems, where we leverage rich auxiliary
observations (eg, product characteristics) to improve decision making with uncertain …

Markov decision processes are a common tool for modeling sequential planning problems
under uncertainty. In almost all realistic situations, the system model cannot be perfectly …

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

Robust Markov decision processes (MDPs) compute reliable solutions for dynamic decision
problems with partially-known transition probabilities. Unfortunately, accounting for …