  In decision-making problem under uncertainty, predicting unknown parameters
is often considered independent of the optimization part. Decision-focused
Learning (DFL) is a task-oriented framework to integrate prediction and
optimization by adapting predictive model to give better decision for the
corresponding task. Here, an inevitable challenge arises when computing
gradients of the optimal decision with respect to the parameters. Existing
researches cope this issue by smoothly reforming surrogate optimization or
construct surrogate loss function that mimic task loss. However, they are
applied to restricted optimization domain or build functions in a local manner
leading a large computational time. In this paper, we propose Input Convex Loss
Network (ICLN), a novel global surrogate loss which can be implemented in a
general DFL paradigm. ICLN learns task loss via Input Convex Neural Networks
which is guaranteed to be convex for some inputs, while keeping the global
structure for the other inputs. This enables ICLN to admit general DFL through
only a single surrogate loss without any sense for choosing appropriate
parametric forms. We confirm effectiveness and flexibility of ICLN by
evaluating our proposed model with three stochastic decision-making problems.
