Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces

Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, Andreas Krause

Abstract: Bayesian optimization is known to be difficult to scale to high dimensions, because the acquisition step requires solving a non-convex optimization problem in the same search space. In order to sca...