BOCK : Bayesian Optimization with Cylindrical Kernels. A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space. In this paper, we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea is to transform the ball geometry of the search space using a cylindrical transformation. Because of the transformed geometry, the Gaussian Process-based surrogate model spends less budget searching near the boundary, while concentrating its efforts relatively more near the center of the search region, where we expect the solution to be located. We evaluate BOCK extensively, showing that it is not only more accurate and efficient, but it also scales successfully to problems with a dimensionality as high as 500. We show that the better accuracy and scalability of BOCK even allows optimizing modestly sized neural network layers, as well as neural network hyperparameters.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Belakaria, Syrine; Deshwal, Aryan; Doppa, Janardhan Rao: Output space entropy search framework for multi-objective Bayesian optimization (2021)
- Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
- Kwon, Yongchan; Kim, Wonyoung; Sugiyama, Masashi; Paik, Myunghee Cho: Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric (2020)
- Zhan, Dawei; Xing, Huanlai: Expected improvement for expensive optimization: a review (2020)
- ChangYong Oh, Efstratios Gavves, Max Welling: BOCK : Bayesian Optimization with Cylindrical Kernels (2019) arXiv