让生成模型训练时考虑决策成本，提升高风险场景预测准确性。
Decision-Aware Training for Sample-Based Generative Models
- 用可微分的决策损失替代传统评分规则，使训练关注高代价区域。
- 在真实任务中验证，关键决策区误差显著降低，整体概率分布仍完整。
- 适合医疗、金融等高风险决策场景，需权衡错误代价的用户使用。
基于样本的生成模型在高风险决策场景中的概率预测中日益重要，但其训练目标未考虑决策者的成本结构。这些模型通常采用严格的适当评分规则（如能量得分）进行训练，其训练信号按数据密度分配，而未关注预测错误对下游决策造成的最大成本区域。为此，我们提出一种面向决策的训练方法，通过在能量得分基础上增加可微分的决策损失，直接惩罚依据模型预测行动所导致的成本。该联合损失具有理论基础，因为决策损失本身也是一种适当评分规则。我们在一个合成任务和两个真实世界任务上验证了该方法，结果表明在成本敏感区域实现了针对性改进，同时保持了完整的概率预测能力。
原文摘要 · Abstract (English)
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.
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