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.

翻译：序列学习方法（如主动学习和贝叶斯优化）选择最具信息量的数据来学习任务。在许多医学或工程应用中，数据选择受限于先验未知的安全条件。一类有前景的安全学习方法利用高斯过程（GPs）对安全概率进行建模，并在具有高安全置信度的区域进行数据选择。然而，精确的安全建模需要先验知识或消耗数据。此外，安全置信度集中在给定观测值周围，这导致局部探索。由于在安全关键实验中通常可获得可转移的源知识，我们提出考虑转移安全序列学习以加速安全学习。我们进一步考虑对源组件进行预计算，以减少引入源数据带来的额外计算负担。本文从理论上分析了传统安全学习方法的最大可探索安全区域。此外，我们的实验表明，该方法能够：1）以更少的数据消耗完成任务学习，2）在源知识的指导下全局探索多个互不相交的安全区域，3）计算开销与传统安全学习方法相当。