How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?

TL;DR

This paper investigates whether intermittent, asynchronous expert feedback can enhance Bayesian optimization in self-driving labs, showing that even sparse, non-blocking feedback can improve efficiency and outcomes.

Contribution

It introduces a method for incorporating randomly arriving, non-blocking expert feedback into Bayesian optimization to improve its performance in autonomous scientific discovery.

Findings

Intermittent expert feedback improves BO performance.

Asynchronous feedback can constrain exploration effectively.

Method enhances data efficiency in self-driving labs.

Abstract

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.