A view on model misspecification in uncertainty quantification
Abstract
Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model misspecification. Model misspecification always exists as models are mere simplifications or approximations to reality. The question arises whether the estimated uncertainty under model misspecification is reliable or not. In this paper, we argue that model misspecification should receive more attention, by providing thought experiments and contextualizing these with relevant literature.
Cite
@article{arxiv.2210.16938,
title = {A view on model misspecification in uncertainty quantification},
author = {Yuko Kato and David M. J. Tax and Marco Loog},
journal= {arXiv preprint arXiv:2210.16938},
year = {2022}
}
Comments
An initial version of the current work has been accepted to be presented at BNAIC/BeNeLearn 2022, to which it was submitted on August 27, 2022