Predict-then-Optimize is a framework for using machine learning to perform decision-making under uncertainty. The central research question it asks is, "How can the structure of a decision-making task be used to tailor ML models for that specific task?" To this end, recent work has proposed learning task-specific loss functions that capture this underlying structure. However, current approaches make restrictive assumptions about the form of these losses and their impact on ML model behavior. These assumptions both lead to approaches with high computational cost, and when they are violated in practice, poor performance. In this paper, we propose solutions to these issues, avoiding the aforementioned assumptions and utilizing the ML model's features to increase the sample efficiency of learning loss functions. We empirically show that our method achieves state-of-the-art results in four domains from the literature, often requiring an order of magnitude fewer samples than comparable methods from past work. Moreover, our approach outperforms the best existing method by nearly 200% when the localness assumption is broken.

翻译：预测后优化是一种利用机器学习在不确定性下进行决策的框架。其核心研究问题是："如何利用决策任务的结构来为该特定任务定制机器学习模型？"为此，近期工作提出了学习任务特定损失函数以捕捉这种底层结构。然而，现有方法对这些损失函数的形式及其对机器学习模型行为的影响做出了限制性假设。这些假设不仅导致方法计算成本高昂，而且当假设在实践中被违反时，性能会变差。本文提出了解决这些问题的方案，避开了前述假设，并利用机器学习模型的特征来提高损失函数学习的样本效率。实验表明，我们的方法在来自文献的四个领域达到了最先进的结果，通常所需样本量比以往工作中类似方法少一个数量级。此外，当局部性假设被打破时，我们的方法性能比现有最佳方法提升了近200%。