Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties
This work addresses the challenge of optimizing expensive experimental designs for domain experts by enhancing BO with human-AI collaboration, though it is incremental as it builds on existing BO methods.
The paper tackles the problem of Bayesian Optimization (BO) learning from scratch by incorporating expert preferences about unmeasured abstract properties into surrogate modeling, resulting in improved performance as shown in experiments with synthetic functions and real-world datasets.
Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive and black-box experimental design processes. While Bayesian optimization is a principled data-driven approach to experimental optimization, it learns everything from scratch and could greatly benefit from the expertise of its human (domain) experts who often reason about systems at different abstraction levels using physical properties that are not necessarily directly measured (or measurable). In this paper, we propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into the surrogate modeling to further boost the performance of BO. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments. We discuss the convergence behavior of our proposed framework. Our experimental results involving synthetic functions and real-world datasets show the superiority of our method against the baselines.