Regret bounds for transfer learning in Bayesian optimisation Shilton, Alistair, Gupta, Sunil, Rana, Santu and Venkatesh, Svetha 2017, Regret bounds for transfer learning in Bayesian optimisation, in AISTATS 2017 : Machine Learning Research : Proceedings of the 20th Artificial Intelligence and Statistics International Conference, Journal of Machine Learning Research (JMLR), [Fort Lauderdale, Fla.], pp. 1-9. Attached Files Name Description MIMEType Size Downloads shilton-regretbounds-2017.pdf Published version application/pdf 349.67KB 132 Title Regret bounds for transfer learning in Bayesian optimisation Author(s) Shilton, Alistair orcid.org/0000-0002-0849-3271 Gupta, Sunil orcid.org/0000-0002-3308-1930 Rana, Santu orcid.org/0000-0003-2247-850X Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Artificial Intelligence and Statistics. International Conference (20th : 2017 : Fort Lauderdale, Florida) Conference location Fort Lauderdale, Florida Conference dates 20-22 Apr. 2017 Title of proceedings AISTATS 2017 : Machine Learning Research : Proceedings of the 20th Artificial Intelligence and Statistics International Conference Editor(s) Singh, Aarti Zhu, Jerry Publication date 2017 Conference series Proceedings of machine learning research, v.54 Start page 1 End page 9 Total pages 9 Publisher Journal of Machine Learning Research (JMLR) Place of publication [Fort Lauderdale, Fla.] Summary This paper studies the regret bound of two transfer learning algorithms in Bayesian optimisation. The first algorithm models any difference between the source and target functions as a noise process. The second algorithm proposes a new way to model the difference between the source and target as a Gaussian process which is then used to adapt the source data. We show that in both cases the regret bounds are tighter than in the no transfer case. We also experimentally compare the performance of these algorithms relative to no transfer learning and demonstrate benefits of transfer learning. Language eng Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 0 Not Applicable HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2017, The Authors Free to Read? Yes Use Rights Persistent URL http://hdl.handle.net/10536/DRO/DU:30094575 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics (PRADA) Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Mon, 01 May 2017, 12:56:01 EST Tue, 02 May 2017, 05:00:23 EST Tue, 02 May 2017, 08:20:22 EST Thu, 25 May 2017, 16:35:41 EST Mon, 29 May 2017, 10:24:43 EST Tue, 30 May 2017, 06:05:41 EST Wed, 31 May 2017, 11:27:04 EST Wed, 31 May 2017, 11:31:48 EST Wed, 09 Aug 2017, 11:36:10 EST Fri, 07 Sep 2018, 12:42:52 EST Sat, 06 Jul 2019, 02:31:08 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 7 times in Scopus Search Google Scholar Access Statistics: 512 Abstract Views, 134 File Downloads  -  Detailed Statistics Created: Mon, 29 May 2017, 10:24:43 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.