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dc.contributor.authorGal, Yarin
dc.contributor.authorTurner, Richard
dc.date.accessioned2015-08-28T11:38:44Z
dc.date.available2015-08-28T11:38:44Z
dc.date.issued2015
dc.identifier.citationJournal of Machine Learning Research Workshop and Conference Proceedings 2015, 655–664.en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/250392
dc.descriptionThis is the final version of the article. It first appeared at http://jmlr.org/proceedings/papers/v37/galb15.htmlen
dc.description.abstractStandard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance function with a finite Fourier series approximation and treat it as a random variable. The random covariance function has a posterior, on which a variational distribution is placed. The variational distribution transforms the random covariance function to fit the data. We study the properties of our approximate inference, compare it to alternative ones, and extend it to the distributed and stochastic domains. Our approximation captures complex functions better than standard approaches and avoids over-fitting.en
dc.description.sponsorshipYG is supported by the Google European Fellowship in Machine Learning. Funding was provided by the EPSRC (grant numbers EP/G050821/1 and EP/L000776/1) and Google (R.E.T.).en
dc.language.isoenen
dc.publisherMicrotome Publishingen
dc.rightsAttribution-NonCommercial 2.0 UK: England & Wales
dc.rights.urihttp://creativecommons.org/licenses/by-nc/2.0/uk/
dc.titleImproving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputsen
dc.typeArticleen
dc.type.versionpublished versionen
prism.endingPage664
prism.publicationDate2015
prism.publicationNameJournal of Machine Learning Research
prism.startingPage655
dc.rioxxterms.funderEPSRC
dc.rioxxterms.projectidEP/G050821/1
dc.rioxxterms.projectidEP/L000776/1
pubs.declined2017-10-11T13:54:38.714+0100
dc.identifier.urlhttp://jmlr.org/proceedings/papers/v37/galb15.html


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Except where otherwise noted, this item's licence is described as Attribution-NonCommercial 2.0 UK: England & Wales