提出可保证决策质量的预测代理方法，提升不确定环境下的优化效果。
Sufficient Decision Proxies for Decision-Focused Learning
- 设计新型决策代理，直接优化决策质量而非预测精度
- 在连续与离散问题中均实现高质量决策，误差显著降低
- 适用于目标函数或约束含不确定性场景，适合优化研究者
在存在上下文数据的不确定性优化问题中，利用机器学习预测不确定参数是常见有效方法。决策聚焦学习（DFL）旨在训练预测模型以最大化决策质量，而非预测准确性。现有做法通常预测单一情景，隐含假设存在能支持最优决策的确定性问题近似（代理）。另一种方法则用参数化分布估计底层分布。然而，何种情况下上述任一选择成立仍不明确。本文首次系统研究了支持特定决策代理的问题特性，并提出了替代性决策代理，几乎不增加学习复杂度。实验表明，该方法在连续与离散问题，以及目标函数和约束中含不确定性的问题上均有效。
原文摘要 · Abstract (English)
When solving optimization problems under uncertainty with contextual data, utilizing machine learning to predict the uncertain parameters' values is a popular and effective approach. Decision-focused learning (DFL) aims at learning a predictive model such that decision quality, instead of prediction accuracy, is maximized. Common practice is to predict a single scenario representing the uncertain parameters, implicitly assuming that there exists a deterministic problem approximation (proxy) that allows for optimal decision-making. The opposite has also been considered, where the underlying distribution is estimated with a parameterized distribution. However, little is known about when either choice is valid. This paper investigates for the first time problem properties that justify using a certain decision proxy. Using this, we present alternative decision proxies for DFL, with little or no compromise on the complexity of the learning task. We show the effectiveness of presented approaches in experiments on continuous and discrete problems, as well as problems with uncertainty in the objective function and in the constraints.
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