Spectral Mixture Kernels for Bayesian Optimization

Bayesian Optimization (BO) is a widely used approach for solving expensive black-box optimization tasks. However, selecting an appropriate probabilistic surrogate model remains an important yet challenging problem. In this work, we introduce a novel Gaussian Process (GP)-based BO method that incorporates spectral mixture kernels, derived from spectral densities formed by scale-location mixtures of Cauchy and Gaussian distributions. This method achieves a significant improvement in both efficiency and optimization performance, matching the computational speed of simpler kernels while delivering results that outperform more complex models and automatic BO methods. We provide bounds on the information gain and cumulative regret associated with obtaining the optimum. Extensive numerical experiments demonstrate that our method consistently outperforms existing baselines across a diverse range of synthetic and real-world problems, including both low- and high-dimensional settings.