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

Adaptive robust optimization problems are usually solved approximately by restricting the
adaptive decisions to simple parametric decision rules. However, the corresponding …

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

We investigate a simple approximation scheme, based on overlap** linear decision rules,
for solving data-driven two-stage distributionally robust optimization problems with the type …

We develop a tractable and flexible data-driven approach for incorporating side information
into multi-stage stochastic programming. The proposed framework uses predictive machine …

We consider a distributionally robust optimization problem where the ambiguity set of
probability distributions is characterized by a tractable conic representable support set and …

We consider stochastic optimization with side information where, prior to decision-making,
covariate data are available to inform better decisions. To hedge against data uncertainty …

This paper considers distributionally robust formulations of a two stage stochastic
programming problem with the objective of minimizing a distortion risk of the minimal cost …

We develop several distributionally robust equilibrium models, following the recent research
surge of robust game theory, in which some or all of the players in the games lack of …

Robust optimization is a popular paradigm for modeling and solving two-and multi-stage
decision-making problems affected by uncertainty. In many real-world applications, the time …