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 behavior of our proposed framework. Our experimental results involving synthetic functions and real-world datasets show the superiority of our method against the baselines.
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Acknowledgements
This research was partially supported by the Australian Government through the Australian Research Council’s Discovery Project funding scheme (project DP210102798). The views expressed herein are those of the authors and are not necessarily those of the Australian Government or Australian Research Council.
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Prof. Svetha Venkatesh has received research grants from Australian Research Council’s Discovery Project funding scheme.
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Arun Kumar, A.V., Shilton, A., Gupta, S., Rana, S., Greenhill, S., Venkatesh, S. (2024). Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties. In: Bifet, A., Davis, J., Krilavičius, T., Kull, M., Ntoutsi, E., Žliobaitė, I. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14946. Springer, Cham. https://doi.org/10.1007/978-3-031-70365-2_14
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