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About this paper
Raul Astudillo, Peter I. Frazier
Grey-box Bayesian Optimization (BO) methods are described, which enhance optimization efficiency by leveraging internal computational information from objective functions. The tutorial categorizes these techniques into composite objective functions, multi-fidelity BO, and objective constituent evaluations, illustrating how exploiting internal structure can dramatically reduce the number of expensive evaluations compared to traditional black-box BO.
Key insights
- By "thinking inside the box" and exploiting internal information, grey-box methods can dramatically accelerate the search for optimal solutions, improving performance by orders of magnitude.
- Modeling the internal structure (e.g., vector-valued functions, correlations between fidelities) provides a more accurate understanding of the objective function's landscape, leading to superior sampling decisions.
- Computational techniques like the reparameterization trick and Sample Average Approximation (SAA) are crucial for making complex, generalized acquisition functions tractable and optimizable in grey-box settings.
Summary by alphaXiv.
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