Topic: contextual data

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.

Robin Braun and Luke Norris discuss agent orchestration for enterprise and municipal workflows. Examples include website accessibility, interpreting deed restrictions and combining contextual data with camera feeds for fire-risk assessment. The conversation examines data freshness, private infrastructure, privacy, cost and incremental deployment.