ML Anthology

Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties

A. V. Arun Kumar, Alistair Shilton, Sunil Gupta, Santu Rana, Stewart Greenhill, Svetha Venkatesh
ECML-PKDD 2024 pp. 234-250
doi:10.1007/978-3-031-70365-2_14 /ecmlpkdd/2024/kumar2024ecmlpkdd-enhanced/

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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Cite

Text

Kumar et al. "Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024. doi:10.1007/978-3-031-70365-2_14

Markdown

[Kumar et al. "Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024.](https://mlanthology.org/ecmlpkdd/2024/kumar2024ecmlpkdd-enhanced/) doi:10.1007/978-3-031-70365-2_14

BibTeX

@inproceedings{kumar2024ecmlpkdd-enhanced,
  title     = {{Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties}},
  author    = {Kumar, A. V. Arun and Shilton, Alistair and Gupta, Sunil and Rana, Santu and Greenhill, Stewart and Venkatesh, Svetha},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2024},
  pages     = {234-250},
  doi       = {10.1007/978-3-031-70365-2_14},
  url       = {https://mlanthology.org/ecmlpkdd/2024/kumar2024ecmlpkdd-enhanced/}
}