Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity.

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 547
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorYang, Eunhoko
dc.contributor.authorLozano, Aurelieko
dc.date.accessioned2017-08-08T05:37:05Z-
dc.date.available2017-08-08T05:37:05Z-
dc.date.created2017-06-10-
dc.date.created2017-06-10-
dc.date.created2017-06-10-
dc.date.issued2017-08-06-
dc.identifier.citationInternational Conference on Machine Learning (ICML) 34-
dc.identifier.urihttp://hdl.handle.net/10203/225034-
dc.languageEnglish-
dc.publisherInternational Machine Learning Society (IMLS)-
dc.titleSparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity.-
dc.typeConference-
dc.identifier.wosid000683309504002-
dc.identifier.scopusid2-s2.0-85048539180-
dc.type.rimsCONF-
dc.citation.publicationnameInternational Conference on Machine Learning (ICML) 34-
dc.identifier.conferencecountryAT-
dc.identifier.conferencelocationInternational Convention Centre Sydney-
dc.contributor.localauthorYang, Eunho-
dc.contributor.nonIdAuthorLozano, Aurelie-
Appears in Collection
AI-Conference Papers(학술대회논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0