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Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties
Feb. 28, 2024, 5:42 a.m. | Arun Kumar A V, Alistair Shilton, Sunil Gupta, Santu Rana, Stewart Greenhill, Svetha Venkatesh
cs.LG updates on arXiv.org arxiv.org
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 …
abstract arxiv bayesian box cs.lg data data-driven design designing driver everything experimental key modeling optimization processes products scratch stat.ml tool type via
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