  Black-box optimization is one of the vital tasks in machine learning, since
it approximates real-world conditions, in that we do not always know all the
properties of a given system, up to knowing almost nothing but the results.
This paper describes our approach to solving the black-box optimization
challenge at NeurIPS 2020 through learning search space partition for local
Bayesian optimization. We describe the task of the challenge as well as our
algorithm for low budget optimization that we named \texttt{SPBOpt}. We
optimize the hyper-parameters of our algorithm for the competition finals using
multi-task Bayesian optimization on results from the first two evaluation
settings. Our approach has ranked third in the competition finals.
