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Efficient Bayesian optimisation using derivative meta-model

Version 2 2024-06-06, 01:32
Version 1 2018-10-25, 13:23
conference contribution
posted on 2024-06-06, 01:32 authored by Leon YangLeon Yang, C Li, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, Svetha VenkateshSvetha Venkatesh
Bayesian 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

Volume

11013

Pagination

256-264

Location

Nanjing, China

Start date

2018-08-28

End date

2018-08-31

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319973098

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2018, Springer International Publishing AG, part of Springer Nature

Editor/Contributor(s)

Geng X, Kang BH

Title of proceedings

PRICAI 2018: Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence

Event

Jiangsu Association of Artificial Intelligence. Conference (15th : 2018 : Nanjing, China)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

Jiangsu Association of Artificial Intelligence Conference

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