  Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive
model to a downstream optimization task that uses its predictions in order to
perform better on that specific task. The main technical challenge associated
with DFL is that it requires being able to differentiate through the
optimization problem, which is difficult due to discontinuous solutions and
other challenges. Past work has largely gotten around this issue by
handcrafting task-specific surrogates to the original optimization problem that
provide informative gradients when differentiated through. However, the need to
handcraft surrogates for each new task limits the usability of DFL. In
addition, there are often no guarantees about the convexity of the resulting
surrogates and, as a result, training a predictive model using them can lead to
inferior local optima. In this paper, we do away with surrogates altogether and
instead learn loss functions that capture task-specific information. To the
best of our knowledge, ours is the first approach that entirely replaces the
optimization component of decision-focused learning with a loss that is
automatically learned. Our approach (a) only requires access to a black-box
oracle that can solve the optimization problem and is thus generalizable, and
(b) can be convex by construction and so can be easily optimized over. We
evaluate our approach on three resource allocation problems from the literature
and find that our approach outperforms learning without taking into account
task structure in all three domains, and even hand-crafted surrogates from the
literature.
