Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties

Arun Kumar A V, Alistair Shilton, Sunil Gupta, Santu Rana, Stewart Greenhill, Svetha Venkatesh

Primary Area: optimization

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Keywords: Human-AI Teaming, Bayesian Optimisation, Preference Learning, Rank Gaussian Process, Thompson Sampling

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TL;DR: Incorporating expert preferences based on abstract properties to further boost the performance of Bayesian optimization

Abstract: 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 details of our proposed framework. The empirical results show the efficacy of our proposed method.

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Submission Number: 6608