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

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Paananen, Topi
dc.contributor.author Piironen, Juho
dc.contributor.author Andersen, Michael
dc.contributor.author Vehtari, Aki
dc.date.accessioned 2019-07-30T07:19:35Z
dc.date.available 2019-07-30T07:19:35Z
dc.date.issued 2019-04-16
dc.identifier.citation Paananen , T , Piironen , J , Andersen , M & Vehtari , A 2019 , Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution . in Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics . Proceedings of Machine Learning Research , vol. 89 , PMLR , International Conference on Artificial Intelligence and Statistics , Naha , Japan , 16/04/2019 . en
dc.identifier.issn 1938-7228
dc.identifier.other PURE UUID: b1e28894-9b21-42af-8428-47b6293a277f
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/variable-selection-for-gaussian-processes-via-sensitivity-analysis-of-the-posterior-predictive-distribution(b1e28894-9b21-42af-8428-47b6293a277f).html
dc.identifier.other PURE LINK: https://arxiv.org/abs/1712.08048
dc.identifier.other PURE LINK: https://github.com/topipa/gp-varsel-kl-var
dc.identifier.other PURE LINK: http://proceedings.mlr.press/v89/paananen19a.html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/35126287/paananen19a.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/39490
dc.description.abstract 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. en
dc.format.extent 10
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher PMLR
dc.relation.ispartof International Conference on Artificial Intelligence and Statistics en
dc.relation.ispartofseries Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics en
dc.relation.ispartofseries Proceedings of Machine Learning Research en
dc.relation.ispartofseries Volume 89 en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Professorship Vehtari A.
dc.contributor.department Probabilistic Machine Learning
dc.contributor.department Department of Computer Science
dc.contributor.department Department of Computer Science en
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201907304545
dc.type.version publishedVersion


Files in this item

Files Size Format View

There are no open access files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search archive


Advanced Search

article-iconSubmit a publication

Browse