Decision-focused learning (DFL) is an emerging paradigm in machine learning which trains a model to optimize decisions, integrating prediction and optimization in an end-to-end system. This paradigm holds the promise to revolutionize decision-making in many real-world applications which operate under uncertainty, where the estimation of unknown parameters within these decision models often becomes a substantial roadblock. This paper presents a comprehensive review of DFL. It provides an in-depth analysis of the various techniques devised to integrate machine learning and optimization models introduces a taxonomy of DFL methods distinguished by their unique characteristics, and conducts an extensive empirical evaluation of these methods proposing suitable benchmark dataset and tasks for DFL. Finally, the study provides valuable insights into current and potential future avenues in DFL research.

翻译：面向决策的学习（DFL）是一种新兴的机器学习范式，它以端到端系统的方式整合预测与优化，训练模型以优化决策。该范式有望在诸多存在不确定性的实际应用中革新决策过程，而此类决策模型中未知参数的估计常常成为主要障碍。本文对DFL进行了全面综述：深入分析了用于融合机器学习与优化模型的各种技术，提出了一个基于独特特征区分DFL方法的分类体系，并通过对这些方法进行广泛的经验性评估，为DFL提出了合适的基准数据集与任务。最后，本研究为当前及未来DFL研究的潜在方向提供了宝贵见解。