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dc.contributor.authorLi, Jen
dc.contributor.authorMantiuk, Rafalen
dc.contributor.authorWang, Jen
dc.contributor.authorLing, Sen
dc.contributor.authorLe Callet, Pen
dc.date.accessioned2019-01-16T00:30:50Z
dc.date.available2019-01-16T00:30:50Z
dc.date.issued2018-01-01en
dc.identifier.issn1049-5258
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/288028
dc.description.abstractIn this paper we present a hybrid active sampling strategy for pairwise Preference aggregation, which aims at recovering the underlying rating of the test candidates from sparse and noisy pairwise labelling. Our method employs Bayesian optimiza- tion framework and Bradley-Terry model to construct the utility function, then to obtain the Expected Information Gain (EIG) of each pair. For computational efficiency, Gaussian-Hermite quadrature is used for estimation of EIG. In this work, a hybrid active sampling strategy is proposed, either using Global Maximum (GM) EIG sampling or Minimum Spanning Tree (MST) sampling in each trial, which is determined by the test budget. The proposed method has been validated on both simulated and real-world datasets, where it shows higher preference aggregation ability than the state-of-the-art methods.
dc.titleHybrid-MST: A hybrid active sampling strategy for pairwise preference aggregationen
dc.typeConference Object
prism.endingPage3485
prism.publicationDate2018en
prism.publicationNameAdvances in Neural Information Processing Systemsen
prism.startingPage3475
prism.volume2018-Decemberen
dc.identifier.doi10.17863/CAM.35347
dcterms.dateAccepted2018-09-05en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-01-01en
dc.contributor.orcidMantiuk, Rafal [0000-0003-2353-0349]
rioxxterms.typeConference Paper/Proceeding/Abstracten
rioxxterms.freetoread.startdate2022-01-15


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