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：决策导向学习（DFL）是机器学习领域的新兴范式，通过端到端系统将预测与优化相融合，训练模型以优化决策。该范式有望在诸多存在不确定性的现实应用场景中革新决策过程——在这些场景中，对决策模型内未知参数的估计往往构成重大障碍。本文对DFL进行了全面综述：深入分析了用于整合机器学习与优化模型的各种技术，构建了基于独特特征的DFL方法分类体系，并通过大规模实证评估提出了适用于DFL的标准化基准数据集与任务。最后，研究为DFL当前及潜在未来研究方向提供了重要见解。