Chained Gaussian Processes

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorSaul, Alanen_US
dc.contributor.authorHensman, Jamesen_US
dc.contributor.authorVehtari, Akien_US
dc.contributor.authorLawrence, Neil D.en_US
dc.contributor.departmentUniversity of Sheffielden_US
dc.contributor.departmentLancaster Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.date.accessioned2016-10-13T06:10:59Z
dc.date.issued2016en_US
dc.description.abstractGaussian process models are flexible, Bayesian non-parametric approaches to regression. Properties of multivariate Gaussians mean that they can be combined linearly in the manner of additive models and via a link function (like in generalized linear models) to handle non-Gaussian data. However, the link function formalism is restrictive, link functions are always invertible and must convert a parameter of interest to an linear combination of the underlying processes. There are many likelihoods and models where a non-linear combination is more appropriate. We term these more general models “Chained Gaussian Processes”: the transformation of the GPs to the likelihood parameters will not generally be invertible, and that implies that linearisation would only be possible with multiple (localized) links, i.e a chain. We develop an approximate inference procedure for Chained GPs that is scalable and applicable to any factorized likelihood. We demonstrate the approximation on a range of likelihood functions.en
dc.description.versionPeer revieweden
dc.format.extent10
dc.format.extent1431-1440
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationSaul , A , Hensman , J , Vehtari , A & Lawrence , N D 2016 , Chained Gaussian Processes . in Journal of Machine Learning Research: Workshop and Conference Proceedings : AISTATS 2016 Proceedings . vol. 51 , Journal of Machine Learning Research: Workshop and Conference Proceedings , vol. 51 , JMLR , pp. 1431-1440 , International Conference on Artificial Intelligence and Statistics , Cadiz , Spain , 09/05/2016 . < http://jmlr.org/proceedings/papers/v51/saul16.pdf >en
dc.identifier.issn1938-7228
dc.identifier.otherPURE UUID: ac38a52d-bba3-4d73-9bc3-8ae1069bf61een_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/ac38a52d-bba3-4d73-9bc3-8ae1069bf61een_US
dc.identifier.otherPURE LINK: http://jmlr.org/proceedings/papers/v51/saul16.pdfen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/6944977/saul16.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/22935
dc.identifier.urnURN:NBN:fi:aalto-201610135035
dc.language.isoenen
dc.relation.ispartofInternational Conference on Artificial Intelligence and Statisticsen
dc.relation.ispartofseriesJournal of Machine Learning Research: Workshop and Conference Proceedingsen
dc.relation.ispartofseriesVolume 51en
dc.rightsopenAccessen
dc.titleChained Gaussian Processesen
dc.typeConference article in proceedingsfi
dc.type.versionpublishedVersion
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