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 …

This paper studies distributionally robust chance constrained programs (DRCCPs), where
the uncertain constraints must be satisfied with at least a probability of a prespecified …

Validating and controlling safety-critical systems in uncertain environments necessitates
probabilistic reachable sets of future state evolutions. The existing methods of computing …

This paper studies chance-constrained stochastic optimization problems with finite support.
It presents an iterative method that solves reduced-size chance-constrained models …

Expected-value-constrained programming (ECP) formulations are a broad class of
stochastic programming problems including integrated chance constraints, risk models, and …