Train generative models to minimize decision costs directly, not just prediction error—this focuses learning on regions where mistakes matter most for your application.
This paper proposes a training method for generative models that makes them aware of decision costs. Instead of training only to predict data accurately everywhere, the method adds a decision loss that penalizes forecast errors in regions where mistakes are most expensive for downstream decisions. The approach combines standard training objectives with cost-sensitive feedback.