  In the last years decision-focused learning framework, also known as
predict-and-optimize, have received increasing attention. In this setting, the
predictions of a machine learning model are used as estimated cost coefficients
in the objective function of a discrete combinatorial optimization problem for
decision making. Decision-focused learning proposes to train the ML models,
often neural network models, by directly optimizing the quality of decisions
made by the optimization solvers. Based on a recent work that proposed a noise
contrastive estimation loss over a subset of the solution space, we observe
that decision-focused learning can more generally be seen as a learning-to-rank
problem, where the goal is to learn an objective function that ranks the
feasible points correctly. This observation is independent of the optimization
method used and of the form of the objective function. We develop pointwise,
pairwise and listwise ranking loss functions, which can be differentiated in
closed form given a subset of solutions. We empirically investigate the quality
of our generic methods compared to existing decision-focused learning
approaches with competitive results. Furthermore, controlling the subset of
solutions allows controlling the runtime considerably, with limited effect on
regret.
