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dc.contributor.authorValera, Isabelen
dc.contributor.authorGhahramani, Zoubinen
dc.contributor.editorPrecup, Den
dc.contributor.editorTeh, YWen
dc.date.accessioned2017-11-27T16:49:37Z
dc.date.available2017-11-27T16:49:37Z
dc.date.issued2017en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/269733
dc.description.abstractA common practice in statistics and machine learning is to assume that the statistical data types (e.g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is known. However, as the availability of real- world data increases, this assumption becomes too restrictive. Data are often heterogeneous, complex, and improperly or incompletely documented. Surprisingly, despite their practical importance, there is still a lack of tools to automatically discover the statistical types of, as well as appropriate likelihood (noise) models for, the variables in a dataset. In this paper, we fill this gap by proposing a Bayesian method, which accurately discovers the statistical data types in both synthetic and real data.
dc.description.sponsorshipHumboldt Research Fellowship for Postdoctoral Researchers, which funded this research during her stay at the Max Planck Institute for Software Systems. ATI Grant EP/N510129/1 EPSRC Grant EP/N014162/1 Google
dc.titleAutomatic Discovery of the Statistical Types of Variables in a Dataset.en
dc.typeConference Object
prism.endingPage3529
prism.publicationDate2017en
prism.publicationNameICMLen
prism.publicationNameProceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017en
prism.startingPage3521
prism.volume70en
dc.identifier.doi10.17863/CAM.11067
dcterms.dateAccepted2017-05-14en
rioxxterms.versionAM*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017en
dc.contributor.orcidGhahramani, Zoubin [0000-0002-7464-6475]
rioxxterms.typeConference Paper/Proceeding/Abstracten
dc.identifier.urlhttp://proceedings.mlr.press/v70/en
rioxxterms.freetoread.startdate2018-08-11


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