Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution

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Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse lengthscale parameter of each input variable as a proxy for variable relevance. This implicitly determined relevance has several drawbacks that prevent the selection of optimal input
variables in terms of predictive performance. To improve on this, we propose two novel variable selection methods for Gaussian process models that utilize the predictions of a full model in the vicinity of the training points and thereby rank the variables based on their predictive relevance. Our empirical results
on synthetic and real world data sets demonstrate improved variable selection compared to automatic relevance determination in terms of variability and predictive performance.


Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Artificial Intelligence and Statistics
Publication statusPublished - 16 Apr 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Artificial Intelligence and Statistics - Naha, Japan
Duration: 16 Apr 201918 Apr 2019
Conference number: 22

Publication series

NameProceedings of Machine Learning Research
ISSN (Print)1938-7228


ConferenceInternational Conference on Artificial Intelligence and Statistics
Abbreviated titleAISTATS

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