Decision-Aware Training for Sample-Based Generative Models

2026-07-01

AI Summary

Existing generative models struggle to align prediction errors with downstream decision costs in high-stakes scenarios. This work proposes a decision-aware training approach that explicitly embeds a differentiable decision loss into the energy score objective, establishing a joint optimization framework grounded in proper scoring rules. By directly penalizing prediction biases that incur high decision costs while preserving full probabilistic forecasts, the method achieves both theoretical rigor and practical relevance. Empirical evaluations on synthetic data and two real-world tasks demonstrate substantial improvements in predictive performance within cost-sensitive regions, without compromising the overall quality of the probabilistic outputs.

Abstract

Sample-based generative models are increasingly used for probabilistic forecasting in high-stakes decision settings, yet their training objectives are blind to the decision maker's cost structure. These models are commonly trained with strictly proper scoring rules, such as the energy score, which allocate their training signal in proportion to data density, with no awareness of where forecast errors are most costly for downstream decisions. We therefore propose decision-aware training for sample-based generative models, augmenting the energy score objective with a differentiable decision loss that directly penalises the cost incurred by acting on the model's forecast. This combined loss is theoretically grounded, as the decision loss is itself a proper scoring rule. We validate our method on one synthetic and two real-world tasks, showing targeted improvements in cost-sensitive regions while retaining full probabilistic forecasts.

Problem

• decision-aware training
• sample-based generative models
• probabilistic forecasting
• cost-sensitive learning
• proper scoring rules

Innovation

• decision-aware training
• sample-based generative models
• proper scoring rules
• differentiable decision loss
• probabilistic forecasting