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Learning bounded subsets of Lp

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dc.contributor.authorMendelson, Shahar
dc.date.accessioned2024-03-14T00:05:38Z
dc.date.issued2021
dc.date.updated2022-11-13T07:16:24Z
dc.description.abstractWe study learning problems in which the underlying class is a bounded subset of Lp and the target Y belongs to Lp. Previously, minimax sample complexity estimates were known under such boundedness assumptions only when p = ∞. We present a sharp sample complexity estimate that holds for any p > 4; it is based on a learning procedure that is suited for heavy-tailed problems.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn0018-9448en_AU
dc.identifier.urihttp://hdl.handle.net/1885/315977
dc.language.isoen_AUen_AU
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)en_AU
dc.rights© 2021 IEEEen_AU
dc.sourceIEEE Transactions on Information Theoryen_AU
dc.subjectStatistical learningen_AU
dc.titleLearning bounded subsets of Lpen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.issue8en_AU
local.bibliographicCitation.lastpage5282en_AU
local.bibliographicCitation.startpage5269en_AU
local.contributor.affiliationMendelson, Shahar, College of Science, ANUen_AU
local.contributor.authoruidMendelson, Shahar, u4011413en_AU
local.description.embargo2099-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor490500 - Statisticsen_AU
local.identifier.absfor490400 - Pure mathematicsen_AU
local.identifier.ariespublicationa383154xPUB19914en_AU
local.identifier.citationvolume67en_AU
local.identifier.doi10.1109/TIT.2021.3083553en_AU
local.identifier.scopusID2-s2.0-85107214379
local.identifier.thomsonIDWOS:000673623300025
local.publisher.urlhttps://ieeexplore.ieee.org/en_AU
local.type.statusPublished Versionen_AU

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