提出非概率框架，让决策模型在不确定性下更鲁棒地做优化。
Generalized Decision Focused Learning under Imprecise Uncertainty--Theoretical Study
- 用区间、污染模型等非概率方式表示认知不确定性
- 将不确定性融入约束条件，提升复杂场景决策质量
- 适合数据稀疏或不确定性强的工业优化问题
决策聚焦学习已成为机器学习与下游优化融合的关键范式。尽管前景广阔，现有方法多依赖概率模型，且仅关注任务目标，忽略了认知不确定性、非概率建模方法以及不确定性在优化约束中的整合难题。本文提出创新框架：(i) 采用区间（最不信息量）、污染模型（混合模型）和概率框（最丰富信息量）等非概率方式表征认知不确定性；(ii) 提出将不确定性纳入约束的方法，扩展决策聚焦学习在受限环境中的应用；(iii) 引入模糊决策理论以应对高不确定性决策场景；(iv) 设计针对数据稀疏的策略。在基准优化问题上的实验表明，该方法显著提升了决策质量与鲁棒性，有效填补了上述空白。
原文摘要 · Abstract (English)
Decision Focused Learning has emerged as a critical paradigm for integrating machine learning with downstream optimisation. Despite its promise, existing methodologies predominantly rely on probabilistic models and focus narrowly on task objectives, overlooking the nuanced challenges posed by epistemic uncertainty, non-probabilistic modelling approaches, and the integration of uncertainty into optimisation constraints. This paper bridges these gaps by introducing innovative frameworks: (i) a non-probabilistic lens for epistemic uncertainty representation, leveraging intervals (the least informative uncertainty model), Contamination (hybrid model), and probability boxes (the most informative uncertainty model); (ii) methodologies to incorporate uncertainty into constraints, expanding Decision-Focused Learning's utility in constrained environments; (iii) the adoption of Imprecise Decision Theory for ambiguity-rich decision-making contexts; and (iv) strategies for addressing sparse data challenges. Empirical evaluations on benchmark optimisation problems demonstrate the efficacy of these approaches in improving decision quality and robustness and dealing with said gaps.
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