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.

在决策问题中，通过学习预测模型与优化相结合可以提供更好的决策，本文提出了一种全局代理损失函数ICLN，通过Input Convex Neural Networks学习任务损失，并在保持全局结构的同时适用于通用DFL框架。

ICLN：面向决策聚焦学习的输入凸损失网络