  Sequential learning methods such as active learning and Bayesian optimization
select the most informative data to learn about a task. In many medical or
engineering applications, the data selection is constrained by a priori unknown
safety conditions. A promissing line of safe learning methods utilize Gaussian
processes (GPs) to model the safety probability and perform data selection in
areas with high safety confidence. However, accurate safety modeling requires
prior knowledge or consumes data. In addition, the safety confidence centers
around the given observations which leads to local exploration. As transferable
source knowledge is often available in safety critical experiments, we propose
to consider transfer safe sequential learning to accelerate the learning of
safety. We further consider a pre-computation of source components to reduce
the additional computational load that is introduced by incorporating source
data. In this paper, we theoretically analyze the maximum explorable safe
regions of conventional safe learning methods. Furthermore, we empirically
demonstrate that our approach 1) learns a task with lower data consumption, 2)
globally explores multiple disjoint safe regions under guidance of the source
knowledge, and 3) operates with computation comparable to conventional safe
learning methods.
