Information-Theoretic Multi-task Learning Framework for Bayesian Optimisation

Ramachandran, Anil, Gupta, Sunil, Rana, Santu and Venkatesh, Svetha 2019, Information-Theoretic Multi-task Learning Framework for Bayesian Optimisation, in AI 2019: Advances in artificial intelligence : Proceedings of the 32nd Australasian Joint Conference on Artificial Intelligence 2019, Springer, Berlin, Germany, pp. 497-509, doi: 10.1007/978-3-030-35288-2_40.

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Title Information-Theoretic Multi-task Learning Framework for Bayesian Optimisation
Author(s) Ramachandran, AnilORCID iD for Ramachandran, Anil orcid.org/0000-0002-9067-4513
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Artificial Intelligence. Conference (32nd : 2019 : Adelaide, South Australia)
Conference location Adelaide, South Australia
Conference dates 2-5 Dec. 2019
Title of proceedings AI 2019: Advances in artificial intelligence : Proceedings of the 32nd Australasian Joint Conference on Artificial Intelligence 2019
Publication date 2019
Series Lecture Notes in Computer Science; v.11919
Start page 497
End page 509
Total pages 12
Publisher Springer
Place of publication Berlin, Germany
ISBN 9783030352875
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-030-35288-2_40
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Grant ID FL170100006
Persistent URL http://hdl.handle.net/10536/DRO/DU:30134117

Document type: Conference Paper
Collection: A2I2 (Applied Artificial Intelligence Institute)
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