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Learning Heteroscedastic Models by Convex Programming under Group Sparsity

hal.structure.identifierCentre de Recherche en Économie et Statistique [CREST]
dc.contributor.authorDalalyan, Arnak S.
HAL ID: 1524
hal.structure.identifierLaboratoire d'Analyse et de Mathématiques Appliquées [LAMA]
dc.contributor.authorHebiri, Mohamed
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorMeziani, Katia
HAL ID: 2110
hal.structure.identifierLaboratoire Traitement et Communication de l'Information [LTCI]
dc.contributor.authorSalmon, Joseph
HAL ID: 170495
ORCID: 0000-0002-3181-0634
dc.date.accessioned2019-10-02T08:48:23Z
dc.date.available2019-10-02T08:48:23Z
dc.date.issued2013
dc.identifier.issn2640-3498
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19957
dc.language.isoenen
dc.subjectheteroscedastic regressionen
dc.subjectgroup sparsityen
dc.subjecttime series predictionen
dc.subject.ddc515en
dc.titleLearning Heteroscedastic Models by Convex Programming under Group Sparsityen
dc.typeCommunication / Conférence
dc.description.abstractenPopular sparse estimation methods based on ℓ1-relaxation, such as the Lasso and the Dantzig selector, require the knowledge of the variance of the noise in order to properly tune the regularization parameter. This constitutes a major obstacle in applying these methods in several frameworks---such as time series, random fields, inverse problems---for which the noise is rarely homoscedastic and its level is hard to know in advance. In this paper, we propose a new approach to the joint estimation of the conditional mean and the conditional variance in a high-dimensional (auto-) regression setting. An attractive feature of the proposed estimator is that it is efficiently computable even for very large scale problems by solving a second-order cone program (SOCP). We present theoretical analysis and numerical results assessing the performance of the proposed procedure.en
dc.identifier.citationpages379–387en
dc.relation.ispartoftitleVolume 28: International Conference on Machine Learning, 17-19 June 2013, Atlanta, Georgia, USAen
dc.relation.ispartofpublnameProceedings of Machine Learning Researchen
dc.relation.ispartofdate2013
dc.relation.ispartofpages1497en
dc.identifier.urlsitehttp://proceedings.mlr.press/v28/dalalyan13.htmlen
dc.subject.ddclabelAnalyseen
dc.relation.conftitleInternational Conference on Machine Learningen
dc.relation.confdate2013-06
dc.relation.confcityAtlantaen
dc.relation.confcountryUnited Statesen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2019-10-02T08:42:39Z
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