Decision-Focused Scenario Learning for Contextual Stochastic Programming

Tito Homem-de-Mello presents decision-focused learning for two-stage stochastic optimization conditioned on contextual data. Recent theory shows that some two-stage stochastic linear programs can be solved using a single, initially unknown scenario. The talk establishes when learned scenario generators induce optimal policies and why one scenario component outside the observed support can suffice. A log-barrier smoothing operator makes that learning possible. The resulting method draws on interior-point optimization and is evaluated on standard contextual stochastic-programming benchmarks.