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dc.contributor.authorHuetter, Jan-Christian Klaus
dc.contributor.authorRigollet, Philippe
dc.date.accessioned2020-06-04T17:41:41Z
dc.date.available2020-06-04T17:41:41Z
dc.date.issued2016
dc.identifier.issn2640-3498
dc.identifier.urihttps://hdl.handle.net/1721.1/125674
dc.description.abstractMotivated by its practical success, we show that the 2D total variation denoiser satisfies a sharp oracle inequality that leads to near optimal rates of estimation for a large class of image models such as bi-isotonic, Hölder smooth and cartoons. Our analysis hinges on properties of the unnormalized Laplacian of the two-dimensional grid such as eigenvector delocalization and spectral decay. We also present extensions to more than two dimensions as well as several other graphs. Key words and phrases: Total variation regularization; TV denoising; sharp oracle inequalities; image denoising; edge Lasso; trend filtering; nonparametric regression; shape constrained regression; minimaxen_US
dc.language.isoen
dc.publisherPMLRen_US
dc.relation.isversionofhttp://proceedings.mlr.press/v49/huetter16.htmlen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleOptimal rates for total variation denoisingen_US
dc.typeArticleen_US
dc.identifier.citationHuetter, Jan-Christian and Philippe Rigollet. "Optimal rates for total variation denoising." 29th Annual Conference on Learning Theory, PMLR 49, (2016): 1115-1146. © 2016 J.-C. Hütter & P. Rigolleten_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematicsen_US
dc.relation.journal29th Annual Conference on Learning Theory, PMLR 49en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-11-19T17:18:16Z
dspace.date.submission2019-11-19T17:18:19Z
mit.metadata.statusComplete


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