Learning Global Pairwise Interactions with Bayesian Neural Networks

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Abstract

Estimating global pairwise interaction effects, i.e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications. We propose a non-parametric probabilistic method for detecting interaction effects of unknown form. First, the relationship between the features and the output is modelled using a Bayesian neural network, capable of representing complex interactions and principled uncertainty. Second, interaction effects and their uncertainty are estimated from the trained model. For the second step, we propose an intuitive global interaction measure: Bayesian Group Expected Hessian (GEH), which aggregates information of local interactions as captured by the Hessian. GEH provides a natural trade-off between type I and type II error and, moreover, comes with theoretical guarantees ensuring that the estimated interaction effects and their uncertainty can be improved by training a more accurate BNN. The method empirically outperforms available non-probabilistic alternatives on simulated and real-world data. Finally, we demonstrate its ability to detect interpretable interactions between higher-level features (at deeper layers of the neural network).
Original languageEnglish
Title of host publicationECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings
PublisherIOS PRESS
Pages1087-1094
Number of pages8
ISBN (Electronic)9781643681009
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventEuropean Conference on Artificial Intelligence - Santiago de Compostela, Spain
Duration: 8 Jun 202012 Jun 2020
Conference number: 24
http://ecai2020.eu/

Publication series

Name Frontiers in Artificial Intelligence and Applications
PublisherIOS Press
Volume325
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

ConferenceEuropean Conference on Artificial Intelligence
Abbreviated titleECAI
CountrySpain
CitySantiago de Compostela
Period08/06/202012/06/2020
Internet address

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