<ul><li>In decision-making problems under uncertainty, predicting unknown parameters is often considered independent of the optimization part.</li><li>Decision-focused learning (DFL) is a task-oriented framework that integrates prediction and optimization by adapting the predictive model to give better decisions for the corresponding task.</li><li>In this paper, the authors propose Locally Convex Global Loss Network (LCGLN), a global surrogate loss model that can be implemented in a general DFL paradigm.</li><li>LCGLN learns task loss via a partial input convex neural network which is guaranteed to be convex for chosen inputs while keeping the non-convex global structure for the other inputs.</li></ul>

Locally Convex Global Loss Network for Decision-Focused Learning

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