Variational Bayesian Decision-making for Continuous Utilities

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dc.contributor.author Kusmierczyk, Tomasz
dc.contributor.author Sakaya, Joseph
dc.contributor.author Klami, Arto
dc.contributor.editor Wallach, H.
dc.contributor.editor Larochelle, H.
dc.contributor.editor Beygelzimer, A.
dc.contributor.editor d'Alché-Buc , F.
dc.contributor.editor Fox, E.
dc.contributor.editor Garnett, R.
dc.date.accessioned 2020-01-22T11:10:01Z
dc.date.available 2020-01-22T11:10:01Z
dc.date.issued 2019-12
dc.identifier.citation Kusmierczyk , T , Sakaya , J & Klami , A 2019 , Variational Bayesian Decision-making for Continuous Utilities . in H Wallach , H Larochelle , A Beygelzimer , F d'Alché-Buc , E Fox & R Garnett (eds) , Advances in Neural Information Processing Systems 32 (NIPS 2019) . Advances in Neural Information Processing Systems , vol. 32 , Morgan Kaufmann Publishers , Maryland Heights, MO , Advances in neural information processing systems , Vancouver , Canada , 08/12/2019 . < https://papers.nips.cc/paper/8868-variational-bayesian-decision-making-for-continuous-utilities.pdf >
dc.identifier.citation conference
dc.identifier.other PURE: 130094132
dc.identifier.other PURE UUID: d792de80-d5b5-4f4b-b2ad-8dc5b996d1fd
dc.identifier.other ORCID: /0000-0002-7950-1355/work/68616178
dc.identifier.other WOS: 000534424306040
dc.identifier.other Scopus: 85090178579
dc.identifier.uri http://hdl.handle.net/10138/310129
dc.description.abstract Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior. However, practitioners often do not have access to the full posterior and resort to approximate inference strategies. In such cases, taking the eventual decision-making task into account while performing the inference allows for calibrating the posterior approximation to maximize the utility. We present an automatic pipeline that co-opts continuous utilities into variational inference algorithms to account for decision-making. We provide practical strategies for approximating and maximizing the gain, and empirically demonstrate consistent improvement when calibrating approximations for specific utilities. en
dc.format.extent 11
dc.language.iso eng
dc.publisher Morgan Kaufmann Publishers
dc.relation.ispartof Advances in Neural Information Processing Systems 32 (NIPS 2019)
dc.relation.ispartofseries Advances in Neural Information Processing Systems
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.title Variational Bayesian Decision-making for Continuous Utilities en
dc.type Conference contribution
dc.contributor.organization Department of Computer Science
dc.contributor.organization Multi-source probabilistic inference research group / Arto Klami
dc.contributor.organization Helsinki Institute for Information Technology
dc.description.reviewstatus Peer reviewed
dc.relation.issn 1049-5258
dc.rights.accesslevel openAccess
dc.type.version publishedVersion
dc.identifier.url https://papers.nips.cc/paper/8868-variational-bayesian-decision-making-for-continuous-utilities.pdf

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