Decision-Focused Learning (DFL) is an emerging learning paradigm that tackles the task of training a machine learning (ML) model to predict missing parameters of an incomplete optimization problem, where the missing parameters are predicted. DFL trains an ML model in an end-to-end system, by integrating the prediction and optimization tasks, providing better alignment of the training and testing objectives. DFL has shown a lot of promise and holds the capacity to revolutionize decision-making in many real-world applications. However, very little is known about the performance of these models under adversarial attacks. We adopt ten unique DFL methods and benchmark their performance under two distinctly focused attacks adapted towards the Predict-then-Optimize problem setting. Our study proposes the hypothesis that the robustness of a model is highly correlated with its ability to find predictions that lead to optimal decisions without deviating from the ground-truth label. Furthermore, we provide insight into how to target the models that violate this condition and show how these models respond differently depending on the achieved optimality at the end of their training cycles.

翻译：决策聚焦学习（DFL）是一种新兴的学习范式，旨在训练机器学习（ML）模型预测不完整优化问题中缺失的参数，其中缺失参数需通过预测得到。DFL通过整合预测与优化任务，在端到端系统中训练ML模型，使训练与测试目标更好对齐。该范式在众多实际应用中展现出巨大潜力，具备革新决策制定能力。然而，当前对这些模型在对抗攻击下的性能表现知之甚少。我们选取十种独特的DFL方法，针对"预测-然后-优化"问题场景设计两种不同类型的攻击，并基准测试其性能。本研究提出假设：模型的鲁棒性与其寻找不偏离真实标签且能导向最优决策的预测能力高度相关。此外，我们揭示了如何针对违反该条件的模型实施攻击，并展示了这些模型在训练周期结束时，基于所达到的最优性水平而呈现的不同响应特征。