End-to-End Learning for Information Gathering

Rares C Cristian, Pavithra Harsha, Georgia Perakis, Brian Quanz

Keywords: end-to-end learning, optimization, machine learning

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.

Submission Number: 74