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Efficient Bayesian optimisation using derivative meta-model
conference contribution
posted on 2018-01-01, 00:00 authored by Leon YangLeon Yang, Cheng Li, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, Svetha VenkateshSvetha VenkateshBayesian optimisation is an efficient method for global optimisation of expensive black-box functions. However, the current Gaussian process based methods cater to functions with arbitrary smoothness, and do not explicitly model the fact that most of the real world optimisation problems are well-behaved functions with only a few peaks. In this paper, we incorporate such shape constraints through the use of a derivative meta-model. The derivative meta-model is built using a Gaussian process with a polynomial kernel and derivative samples from this meta-model are used as extra observations to the standard Bayesian optimisation procedure. We provide a Bayesian framework to infer the degree of the polynomial kernel. Experiments on both benchmark functions and hyperparameter tuning problems demonstrate the superiority of our approach over baselines.
History
Event
Jiangsu Association of Artificial Intelligence. Conference (15th : 2018 : Nanjing, China)Volume
11013Series
Jiangsu Association of Artificial Intelligence ConferencePagination
256 - 264Publisher
SpringerLocation
Nanjing, ChinaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-08-28End date
2018-08-31ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319973098Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, Springer International Publishing AG, part of Springer NatureEditor/Contributor(s)
X Geng, B KangTitle of proceedings
PRICAI 2018: Proceedings of the 15th Pacific Rim International Conference on Artificial IntelligenceUsage metrics
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