Bayesian Optimization Augmented with Actively Elicited Expert Knowledge
This addresses the challenge of costly function evaluations in optimization for researchers and practitioners, though it is incremental as it builds on existing Bayesian optimization methods.
The paper tackles the problem of accelerating Bayesian optimization by incorporating expert knowledge through a multi-task learning architecture, resulting in significant speed-ups in experiments with benchmark functions, even when the expert knowledge is biased.
Bayesian optimization (BO) is a well-established method to optimize black-box functions whose direct evaluations are costly. In this paper, we tackle the problem of incorporating expert knowledge into BO, with the goal of further accelerating the optimization, which has received very little attention so far. We design a multi-task learning architecture for this task, with the goal of jointly eliciting the expert knowledge and minimizing the objective function. In particular, this allows for the expert knowledge to be transferred into the BO task. We introduce a specific architecture based on Siamese neural networks to handle the knowledge elicitation from pairwise queries. Experiments on various benchmark functions with both simulated and actual human experts show that the proposed method significantly speeds up BO even when the expert knowledge is biased compared to the objective function.