Bayesian optimization (BO) is an integral part of automated scientific
discovery -- the so-called self-driving lab -- where human inputs are ideally
minimal or at least non-blocking. However, scientists often have strong
intuition, and thus human feedback is still useful. Nevertheless, prior works
in enhancing BO with expert feedback, such as by incorporating it in an offline
or online but blocking (arrives at each BO iteration) manner, are incompatible
with the spirit of self-driving labs. In this work, we study whether a small
amount of randomly arriving expert feedback that is being incorporated in a
non-blocking manner can improve a BO campaign. To this end, we run an
additional, independent computing thread on top of the BO loop to handle the
feedback-gathering process. The gathered feedback is used to learn a Bayesian
preference model that can readily be incorporated into the BO thread, to steer
its exploration-exploitation process. Experiments on toy and chemistry datasets
suggest that even just a few intermittent, asynchronous expert feedback can be
useful for improving or constraining BO. This can especially be useful for its
implication in improving self-driving labs, e.g. making them more
data-efficient and less costly.

用一种非阻塞的方式引入少量的随机专家反馈来改善贝叶斯优化，进而在自动科学发现中提高探索性能和数据效率。