Related papers: Careful! Training Relevance is Real

Careful! Training Relevance is Real

URL: http://arxiv.org/abs/2201.04429v1
Date: Wed, 12 Jan 2022 11:54:31 GMT
Title: Careful! Training Relevance is Real
Authors: Chenbo Shi, Mohsen Emadikhiav, Leonardo Lozano, David Bergman
Abstract summary: We propose constraints designed to enforce training relevance. We show through a collection of experimental results that adding the suggested constraints significantly improves the quality of solutions.
Score: 0.7742297876120561
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: There is a recent proliferation of research on the integration of machine learning and optimization. One expansive area within this research stream is predictive-model embedded optimization, which uses pre-trained predictive models for the objective function of an optimization problem, so that features of the predictive models become decision variables in the optimization problem. Despite a recent surge in publications in this area, one aspect of this decision-making pipeline that has been largely overlooked is training relevance, i.e., ensuring that solutions to the optimization problem should be similar to the data used to train the predictive models. In this paper, we propose constraints designed to enforce training relevance, and show through a collection of experimental results that adding the suggested constraints significantly improves the quality of solutions obtained.

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