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.

Decision-Focused Learning（DFL）是一种新兴的学习范例，用于训练机器学习模型来预测不完整优化问题的缺失参数。DFL通过集成预测和优化任务，在端到端系统中训练机器学习模型，从而更好地匹配训练和测试目标。然而，我们对这些模型在对抗性攻击下的性能知之甚少。我们采用了十种独特的DFL方法，并在针对预测优化问题设定的两种明确攻击下进行性能基准测试。我们的研究提出了假设，即模型的鲁棒性与其能否找到导致最佳决策且不偏离基本真实标签的预测高度相关。此外，我们深入洞察了如何针对违反此条件的模型，并展示了这些模型如何根据训练周期结束时实现的最优性而有不同的响应。

关于决策聚焦学习的鲁棒性研究