Accelerated Bayesian Optimization through Weight-Prior Tuning

Shilton, Alistair, Gupta, Sunil, Rana, Santu, Vellanki, P, Park, L, Li, C, Venkatesh, Svetha, Dorin, Thomas, Sutti, Alessandra, Rubin, D, Slezak, Teo, Vahid, Alireza and Height, M 2020, Accelerated Bayesian Optimization through Weight-Prior Tuning, in AISTATS 2020 : Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, Addison-Wesley, [Palermo, Italy], pp. 1-10.

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Title Accelerated Bayesian Optimization through Weight-Prior Tuning
Author(s) Shilton, AlistairORCID iD for Shilton, Alistair orcid.org/0000-0002-0849-3271
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Vellanki, P
Park, L
Li, C
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Dorin, ThomasORCID iD for Dorin, Thomas orcid.org/0000-0002-1033-961X
Sutti, AlessandraORCID iD for Sutti, Alessandra orcid.org/0000-0002-1793-3881
Rubin, D
Slezak, Teo
Vahid, AlirezaORCID iD for Vahid, Alireza orcid.org/0000-0003-2854-2218
Height, M
Conference name Artificial Intelligence and Statistics. Conference (2020 23rd : Online from Palermo, Italy)
Conference location Online from Palermo, Italy
Conference dates 26-28 Aug. 2020
Title of proceedings AISTATS 2020 : Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics
Editor(s) Chiappa, S
Calandra, R
Publication date 2020
Series Proceedings of Machine Learning Research
Start page 1
End page 10
Total pages 10
Publisher Addison-Wesley
Place of publication [Palermo, Italy]
Keyword(s) Science & Technology
Technology
Physical Sciences
Computer Science, Artificial Intelligence
Statistics & Probability
Computer Science
Mathematics
KERNEL BANACH-SPACES
GLOBAL OPTIMIZATION
ISSN 2640-3498
Language eng
Indigenous content off
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Grant ID ARC Australian Laureate Fellowship (FL170100006)
Australian Government through the Australian Research Council (ARC)
Persistent URL http://hdl.handle.net/10536/DRO/DU:30144668

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