  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.
