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dc.contributor.authorÁlvarez, MAen
dc.contributor.authorLuengo, Den
dc.contributor.authorTitsias, MKen
dc.contributor.authorLawrence, Neilen
dc.date.accessioned2020-01-30T00:31:34Z
dc.date.available2020-01-30T00:31:34Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301464
dc.description.abstractInterest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Alvarez and Lawrence (2009) recently presented a sparse approximation for CPs that enabled efficient inference. In this paper, we extend this work in two directions: we introduce the concept of variational inducing functions to handle potential non-smooth functions involved in the kernel CP construction and we consider an alternative approach to approximate inference based on variational methods, extending the work by Titsias (2009) to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.
dc.rightsAll rights reserved
dc.rights.uri
dc.titleVariational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processesen
dc.typeConference Object
prism.publicationNameProceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010en
dc.identifier.doi10.17863/CAM.48536
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
dc.contributor.orcidLawrence, Neil [0000-0001-9258-1030]
rioxxterms.typeConference Paper/Proceeding/Abstracten
cam.issuedOnline2010en
dc.identifier.urlhttp://proceedings.mlr.press/v9/alvarez10a.htmlen
pubs.conference-name13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010en
pubs.conference-start-date2010-05-13en


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