A warped kernel improving robustness in Bayesian optimization via random embeddings

The scope of Bayesian Optimization methods is usually limited to moderate-dimensional problems [1]. [2] recently proposed to extend the applicability of these methods to up to billions of variables, when only few of them are actually influential, through the so-called Random EMbedding Bayesian Optimization (REMBO) approach. In REMBO