Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning

ArXi:2307.05213v3 Announce Type: replace-cross Many real-world optimization problems contain parameters that are unknown before deployment time, either due to stochasticity or to lack of information (e.g., demand or travel times in delivery problems). A common strategy in such cases is to estimate said parameters via machine learning (ML) models trained to minimize the prediction error, which. however. is not necessarily aligned with the downstream task-level error. The decision-focused learning (DFL) paradigm overcomes this limitation by.