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dc.rights.licenseopenen_US
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBIGOT, Jeremie
IDREF: 075404877
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorFREULON, Paul
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHEJBLUM, Boris
ORCID: 0000-0003-0646-452X
IDREF: 189970316
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorLECLAIRE, Arthur
dc.date.accessioned2023-03-08T10:36:44Z
dc.date.available2023-03-08T10:36:44Z
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/172214
dc.description.abstractEnThis paper is focused on the study of entropic regularization in optimal transport as a smoothing method for Wasserstein estimators, through the prism of the classical tradeoff between approximation and estimation errors in statistics. Wasserstein estimators are defined as solutions of variational problems whose objective function involves the use of an optimal transport cost between probability measures. Such estimators can be regularized by replacing the optimal transport cost by its regularized version using an entropy penalty on the transport plan. The use of such a regularization has a potentially significant smoothing effect on the resulting estimators. In this work, we investigate its potential benefits on the approximation and estimation properties of regularized Wasserstein estimators. Our main contribution is to discuss how entropic regularization may reach, at a lowest computational cost, statistical performances that are comparable to those of un-regularized Wasserstein estimators in statistical learning problems involving distributional data analysis. To this end, we present new theoretical results on the convergence of regularized Wasserstein estimators. We also study their numerical performances using simulated and real data in the supervised learning problem of proportions estimation in mixture models using optimal transport.
dc.language.isoENen_US
dc.title.enOn the potential benefits of entropic regularization for smoothing Wasserstein estimators
dc.typeDocument de travail - Pré-publicationen_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.teamSISTM_BPHen_US
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=BIGOT,%20Jeremie&FREULON,%20Paul&HEJBLUM,%20Boris&LECLAIRE,%20Arthur&rft.genre=preprint


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