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hal.structure.identifierUNIVERSITY OF OXFORD GBR
dc.contributor.authorGhalebikesabi, Sahra
dc.contributor.authorCornish, Rob
hal.structure.identifierDepartment of Statistics [Oxford]
dc.contributor.authorHolmes, Chris
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorKelly, Luke Joseph
dc.date.accessioned2023-02-27T09:25:42Z
dc.date.available2023-02-27T09:25:42Z
dc.date.issued2021
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/24482
dc.language.isoenen
dc.subject.ddc515en
dc.titleDeep Generative Missingness Pattern-Set Mixture Modelsen
dc.typeCommunication / Conférence
dc.description.abstractenWe propose a variational autoencoder architecture to model both ignorable and nonignorable missing data using pattern-set mixtures as proposed by Little (1993). Our model explicitly learns to cluster the missing data into missingness pattern sets based on the observed data and missingness masks. Underpinning our approach is the assumption that the data distribution under missingness is probabilistically semi-supervised by samples from the observed data distribution. Our setup trades off the characteristics of ignorable and nonignorable missingness and can thus be applied to data of both types. We evaluate our method on a wide range of data sets with different types of missingness and achieve state-of-the-art imputation performance. Our model outperforms many common imputation algorithms, especially when the amount of missing data is high and the missingness mechanism is nonignorable.en
dc.identifier.citationpages3727-3735en
dc.relation.ispartoftitleProceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR Vol. 130en
dc.relation.ispartofeditorArindam Banerjee, Kenji Fukumizu
dc.relation.ispartofpublnameProceedings of Machine Learning Research (PMLR)en
dc.subject.ddclabelAnalyseen
dc.relation.conftitle24th International Conference on Artificial Intelligence and Statisticsen
dc.relation.confdate2021-04
dc.relation.confcityVirtualen
dc.relation.confcountryFranceen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2023-02-27T09:20:44Z
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