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dc.contributor.authorNguyen, Quoc Phong
dc.contributor.authorLow, Bryan Kian Hsiang
dc.contributor.authorJaillet, Patrick
dc.contributor.authorIntelligence, Assoc Advancement Artificial
dc.date.accessioned2022-07-12T17:50:03Z
dc.date.available2022-07-12T17:50:03Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/143689
dc.language.isoen
dc.relation.isversionofhttps://aaai.org/Library/AAAI/aaai21contents.phpen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceAssociation for the Advancement of Artificial Intelligence (AAAI)en_US
dc.titleAn Information-Theoretic Framework for Unifying Active Learning Problemsen_US
dc.typeArticleen_US
dc.identifier.citationNguyen, Quoc Phong, Low, Bryan Kian Hsiang, Jaillet, Patrick and Intelligence, Assoc Advancement Artificial. 2021. "An Information-Theoretic Framework for Unifying Active Learning Problems." THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 35.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.relation.journalTHIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-07-12T17:46:04Z
dspace.orderedauthorsNguyen, QP; Low, BKH; Jaillet, P; Intelligence, AAAen_US
dspace.date.submission2022-07-12T17:46:05Z
mit.journal.volume35en_US
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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