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oai:drops-oai.dagstuhl.de:4268 2024-03-06T10:29:24Z ddc:004 open_access

Conformal Prediction under Hypergraphical Models Fedorova, Valentina Gammerman, Alex Nouretdinov, Ilia Vovk, Vladimir conformal prediction hypergraphical models conformity measure Conformal predictors are usually defined and studied under the exchangeability assumption. However, their definition can be extended to a wide class of statistical models, called online compression models, while retaining their property of automatic validity. This paper is devoted to conformal prediction under hypergraphical models that are more specific than the exchangeability model. We define conformity measures for such hypergraphical models and study the corresponding conformal predictors empirically on benchmark LED data sets. Our experiments show that they are more efficient than conformal predictors that use only the exchangeability assumption. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Valentina Fedorova and Alex Gammerman and Ilia Nouretdinov and Vladimir Vovk 2013 Is Part Of OASIcs, Volume 35, 2013 Imperial College Computing Student Workshop InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.ICCSW.2013.27 urn:nbn:de:0030-drops-42685 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2013.27 eng https://creativecommons.org/licenses/by/3.0/legalcode