Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination

Zhang, T zhu, T Li, J Han, M Zhou, W Yu, P

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2021, PP, (99), pp. 1-1
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Zhang, T
dc.contributor.author	zhu, T
dc.contributor.author	Li, J
dc.contributor.author	Han, M
dc.contributor.author	Zhou, W
dc.contributor.author	Yu, P
dc.date.accessioned	2021-03-05T02:50:54Z
dc.date.available	2021-03-05T02:50:54Z
dc.date.issued	2021
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2021, PP, (99), pp. 1-1
dc.identifier.issn	1041-4347
dc.identifier.issn	2326-3865
dc.identifier.uri	http://hdl.handle.net/10453/146803
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation	http://purl.org/au-research/grants/arc/DP190100981
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/tkde.2020.3002567
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.title	Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-05T02:50:52Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

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