  The tuning of hyperparameters becomes increasingly important as machine
learning (ML) models have been extensively applied in data mining applications.
Among various approaches, Bayesian optimization (BO) is a successful
methodology to tune hyper-parameters automatically. While traditional methods
optimize each tuning task in isolation, there has been recent interest in
speeding up BO by transferring knowledge across previous tasks. In this work,
we introduce an automatic method to design the BO search space with the aid of
tuning history from past tasks. This simple yet effective approach can be used
to endow many existing BO methods with transfer learning capabilities. In
addition, it enjoys the three advantages: universality, generality, and
safeness. The extensive experiments show that our approach considerably boosts
BO by designing a promising and compact search space instead of using the
entire space, and outperforms the state-of-the-arts on a wide range of
benchmarks, including machine learning and deep learning tuning tasks, and
neural architecture search.
