Abstract

Bayesian optimization is renowned for its sample
efficiency but its application to higher dimensional
tasks is impeded by its focus on global
optimization. To scale to higher dimensional
problems, we leverage the sample efficiency of
Bayesian optimization in a local context. The
optimization of the acquisition function is restricted
to the vicinity of a Gaussian search distribution
which is moved towards high value areas
of the objective. The proposed informationtheoretic
update of the search distribution results
in a Bayesian interpretation of local stochastic
search: the search distribution encodes prior
knowledge on the optimum’s location and is
weighted at each iteration by the likelihood of
this location’s optimality. We demonstrate the
effectiveness of our algorithm on several benchmark
objective functions as well as a continuous
robotic task in which an informative prior is obtained
by imitation learning.