# BOCK : Bayesian Optimization with Cylindrical Kernels

arXiv: https://arxiv.org/abs/1806.01619 · HTML: https://mlsanity.com/paper/1806.01619/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2018.

## Built on by (most cited first, 30 of 65)

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- [OpenBox: A Python Toolkit for Generalized Black-box Optimization](https://mlsanity.com/paper/2304.13339/) (2023)
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- [High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy](https://mlsanity.com/paper/2402.03104/) (2024)
- [Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins](https://mlsanity.com/paper/2106.15502/) (2021)
- [Bayesian Optimization For Multi-Objective Mixed-Variable Problems](https://mlsanity.com/paper/2201.12767/) (2022)
- [Pre-training helps Bayesian optimization too](https://mlsanity.com/paper/2207.03084/) (2022)
- [Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2302.08298/) (2023)
- [Mixed Variable Bayesian Optimization with Frequency Modulated Kernels](https://mlsanity.com/paper/2102.12792/) (2021)
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## Abstract (arXiv)

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.