Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
This addresses the problem of scaling Bayesian optimization to high-dimensional black-box functions, which is important for practitioners in fields like hyperparameter tuning and engineering design, though it appears incremental as it builds on existing BO frameworks.
The paper tackles the challenge of high-dimensional Bayesian optimization by assuming a latent additive structure in the function and performing multiple evaluations in parallel, demonstrating that the proposed method outperforms existing state-of-the-art approaches in experiments on synthetic and real-world functions.
Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box functions, its applicability has been limited to low-dimensional problems due to its computational and statistical challenges arising from high-dimensional settings. In this paper, we propose to tackle these challenges by (1) assuming a latent additive structure in the function and inferring it properly for more efficient and effective BO, and (2) performing multiple evaluations in parallel to reduce the number of iterations required by the method. Our novel approach learns the latent structure with Gibbs sampling and constructs batched queries using determinantal point processes. Experimental validations on both synthetic and real-world functions demonstrate that the proposed method outperforms the existing state-of-the-art approaches.