Enhanced Bayesian Optimization via Preferential Modeling of Abstract
  Properties
Arun Kumar A V Alistair Shilton Sunil Gupta Santu Rana Stewart Greenhill Svetha Venkatesh
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.