  When hyperparameter optimization of a machine learning algorithm is repeated
for multiple datasets it is possible to transfer knowledge to an optimization
run on a new dataset. We develop a new hyperparameter-free ensemble model for
Bayesian optimization that is a generalization of two existing transfer
learning extensions to Bayesian optimization and establish a worst-case bound
compared to vanilla Bayesian optimization. Using a large collection of
hyperparameter optimization benchmark problems, we demonstrate that our
contributions substantially reduce optimization time compared to standard
Gaussian process-based Bayesian optimization and improve over the current
state-of-the-art for transfer hyperparameter optimization.
