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Bayesian Optimization with Discrete Variables
Version 2 2023-10-12, 03:11
Version 1 2023-10-06, 05:00
conference contribution
posted on 2023-10-12, 03:11 authored by P Luong, Sunil GuptaSunil Gupta, Dang NguyenDang Nguyen, Santu RanaSantu Rana, Svetha VenkateshSvetha VenkateshBayesian Optimization (BO) is an efficient method to optimize an expensive black-box function with continuous variables. However, in many cases, the function has only discrete variables as inputs, which cannot be optimized by traditional BO methods. A typical approach to optimize such functions assumes the objective function is on a continuous domain, then applies a normal BO method with a rounding of suggested continuous points to nearest discrete points at the end. This may cause BO to get stuck and repeat pre-existing observations. To overcome this problem, we propose a method (named Discrete-BO) that manipulates the exploration of an acquisition function and the length scale of a covariance function, which are two key components of a BO method, to prevent sampling a pre-existing observation. Our experiments on both synthetic and real-world applications show that the proposed method outperforms state-of-the-art baselines in terms of convergence rate. More importantly, we also show some theoretical analyses to prove the correctness of our method.
History
Volume
11919Pagination
473 - 484Location
Adelaide, South AustraliaPublisher DOI
Open access
- Yes
Start date
2019-12-02End date
2019-12-05ISSN
0302-9743eISSN
1611-3349ISBN-13
9783030352875Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
J Liu, J BaileyTitle of proceedings
AI 2019 : Advances in Artificial Intelligence : Proceedings of the 32nd Australian Joint ConferenceUsage metrics
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