  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.
