Poster
in
Workshop: ML x OR: Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making

End-to-End Learning for Information Gathering

Rares Cristian ⋅ Pavithra Harsha ⋅ Georgia Perakis ⋅ Brian Quanz

Project Page [ OpenReview]

Abstract

This paper introduces an end-to-end, or joint prediction and optimization, framework for the class of two-stage contextual optimization problems with information-gathering. We showcase the approach on a dynamic electricity-scheduling problem on real data. We show that the adaptiveness of the end-to-end approach indeed provides benefits over other methods which train their forecasting method independently of the first information-gathering stage.

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