BOCK : Bayesian Optimization with Cylindrical Kernels
This addresses a specific bottleneck for researchers and practitioners using Bayesian Optimization, offering incremental improvements in performance and scalability.
The paper tackles the boundary issue in Bayesian Optimization, where algorithms waste evaluations near search space boundaries, by proposing BOCK, which uses cylindrical kernels to transform the geometry, resulting in improved accuracy, efficiency, and scalability up to 500 dimensions.
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.