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dc.contributor.authorYamane, Ikko
dc.contributor.authorYger, Florian
dc.contributor.authorAtif, Jamal
dc.contributor.authorSugiyama, Masashi
dc.date.accessioned2019-07-02T10:21:03Z
dc.date.available2019-07-02T10:21:03Z
dc.date.issued2018
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19105
dc.language.isoenen
dc.subjectUplift modelingen
dc.subject.ddc005en
dc.titleUplift Modeling from Separate Labelsen
dc.typeCommunication / Conférence
dc.description.abstractenUplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (advertisement campaigns) and personalized medicine (medical treatments). Conventional methods of uplift modeling require every instance to be jointly equipped with two types of labels: the taken action and its outcome. However, obtaining two labels for each instance at the same time is difficult or expensive in many real-world problems. In this paper, we propose a novel method of uplift modeling that is applicable to a more practical setting where only one type of labels is available for each instance. We show a mean squared error bound for the proposed estimator and demonstrate its effectiveness through experiments.en
dc.identifier.citationpages9927--9937en
dc.relation.ispartoftitleAdvances in Neural Information Processing Systems 31 (NIPS 2018)en
dc.relation.ispartofeditorBengio, Samy
dc.relation.ispartofeditorWallach, Hanna
dc.relation.ispartofeditorLarochelle, Hugo
dc.relation.ispartofeditorGrauman, Kristen
dc.relation.ispartofeditorCesa-Bianchi, Nicolò
dc.relation.ispartofeditorGarnett, Roman
dc.relation.ispartofpublnameNeural Information Processing Systems Foundation, Inc.en
dc.relation.ispartofdate2018
dc.contributor.countryeditoruniversityotherJAPAN
dc.subject.ddclabelProgrammation, logiciels, organisation des donnéesen
dc.relation.conftitle32nd Conference on Neural Information Processing Systems (NeurIPS 2018)en
dc.relation.confdate2018-12
dc.relation.confcityMontréalen
dc.relation.confcountryCanadaen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2019-03-29T18:02:20Z
hal.person.labIds92160
hal.person.labIds989
hal.person.labIds989
hal.person.labIds21538
hal.identifierhal-02170693*


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