Decision-Aware Training for Sample-Based Generative Models

arXiv·low signal

This paper (2607.01171, cs.LG/stat.ML) proposes training objectives for sample-based generative forecasting models that account for the downstream decision they inform, rather than optimizing pure likelihood. Useful for practitioners using generative models for probabilistic forecasting in high-stakes settings where the metric that matters is decision quality.