MetaGB: A Gradient Boosting Framework for Efficient Task Adaptive Meta Learning

Field	Value	Language
dc.contributor.author	Dong, M
dc.contributor.author	Yao, L
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.contributor.author	Xu, X
dc.contributor.author	Zhu, L
dc.date	2021-12-07
dc.date.accessioned	2021-11-02T06:08:36Z
dc.date.available	2021-08-31
dc.date.available	2021-11-02T06:08:36Z
dc.date.issued	2022-04-13
dc.identifier.citation	2022
dc.identifier.uri	http://hdl.handle.net/10453/151308
dc.language	en
dc.relation	http://purl.org/au-research/grants/arc/LP180100654
dc.relation	http://purl.org/au-research/grants/arc/DE180100251
dc.relation.ispartof	IEEE International Conference on Data Mining
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	MetaGB: A Gradient Boosting Framework for Efficient Task Adaptive Meta Learning
dc.type	Conference Proceeding
utslib.location.activity	Auckland, New Zealand
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
utslib.copyright.embargo	2024-04-30T00:00:00+1000Z
dc.date.updated	2021-11-02T06:08:35Z
pubs.finish-date	2021-12-10
pubs.publication-status	Accepted
pubs.start-date	2021-12-07

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