发表机构
University of Augsburg, Germany（奥格斯堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对样本生成模型训练目标忽视下游决策成本的问题，提出将可微决策损失与能量分数结合，实现决策感知训练，在保持完整概率预测的同时提升成本敏感区域性能。
AI 中文摘要
样本生成模型越来越多地用于高风险决策场景中的概率预测，然而其训练目标忽略了决策者的成本结构。这些模型通常使用严格适当的评分规则（如能量分数）进行训练，这些规则根据数据密度分配训练信号，而不考虑预测误差对下游决策成本最大的区域。因此，我们提出面向样本生成模型的决策感知训练，用可微决策损失增强能量分数目标，该损失直接惩罚基于模型预测行动所产生的成本。这种组合损失具有理论基础，因为决策损失本身就是一个适当的评分规则。我们在一个合成任务和两个真实世界任务上验证了我们的方法，显示出在成本敏感区域的针对性改进，同时保留了完整的概率预测。
英文摘要
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 compute the decision loss via a differentiable optimisation layer. Its gradient concentrates in cost-sensitive regions of the output space, making the method's effects interpretable and predictable from the cost structure. We validate the method on one synthetic and two real-world tasks. In the synthetic task, the method corrects the mode weights of a learned bimodal distribution; in a wind power dispatch task, it concentrates improvements in the rare but costly tail region, and in a frost protection task, it improves how well the decision costs are anticipated. Our method yields generative models that retain full probabilistic forecasts while being better aligned with the decision maker's specific cost structure.
