Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimisation task that uses its predictions, so that it can perform better on that specific task. The main technical challenge associated with DFL is that it requires being able to differentiate through $argmin$ operations to work. However, these $argmin$ optimisations are often piecewise constant and, as a result, naively differentiating through them would provide uninformative gradients. Past work has largely focused on getting around this issue by handcrafting task-specific surrogates to the original optimisation problem that provide informative gradients when differentiated through. However, finding these surrogates can be challenging and the need to handcraft surrogates for each new task limits the usability of DFL. In addition, even after applying these relaxation techniques, there are no guarantees that the resulting surrogates are convex and, as a result, training a predictive model on them may lead to said model getting stuck in local minimas. In this paper, we provide an approach to learn faithful task-specific surrogates which (a) only requires access to a black-box oracle that can solve the optimisation problem and is thus generalizable, and (b) can be convex by construction and so can be easily optimized over. To the best of our knowledge, this is the first work on using learning to find good surrogates for DFL. We evaluate our approach on a budget allocation problem from the literature and find that our approach outperforms even the hand-crafted (non-convex) surrogate loss proposed by the original paper. Taking a step back, we hope that the generality and simplicity of our approach will help lower the barrier associated with implementing DFL-based solutions in practice. To that end, we are currently working on extending our experiments to more domains.

本文介绍了一种全新的决策化学习方法，通过学习任务特定的损失函数代替了传统的基于代理的优化方法，与先前的工作相比，该方法不需要手工制定基于任务的代理，性能更好且更易用。

无需可微分优化的决策导向学习: 学习局部优化的决策损失