152 citations · 155 across the 9 of their papers we have counts for
3 papers · 1 filter
Adaptive Partitioning for Chance-Constrained Problems with Finite Support
Marius Roland, Alexandre Forel, Thibaut Vidal
This paper studies chance-constrained stochastic optimization problems with finite support. It presents an iterative method that solves reduced-size chance-constrained models obtai…
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Utsav Sadana, Abhilash Chenreddy, Erick Delage +3
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 so…
Explainable Data-Driven Optimization: From Context to Decision and Back Again
Alexandre Forel, Axel Parmentier, Thibaut Vidal
Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is…