Gradient-based Sample Selection for Faster Bayesian Optimization

Abstract:Bayesian optimization (BO) is an effective technique for black-box optimization. However, its applicability is typically limited to moderate-budget problems due to the cubic complexity of fitting the Gaussian process (GP) surrogate model. In large-budget scenarios, directly employing the standard GP model faces significant challenges in computational time and resource requirements. In this paper, we propose a novel approach, gradient-based sample selection Bayesian Optimization (GSSBO), to enhance the computational efficiency of BO. The GP model is constructed on a selected set of samples instead of the whole dataset. These samples are selected by leveraging gradient information to remove redundancy while preserving diversity and representativeness. We provide a theoretical analysis of the gradient-based sample selection strategy and obtain explicit sublinear regret bounds for our proposed framework. Extensive experiments on synthetic and real-world tasks demonstrate that our approach significantly reduces the computational cost of GP fitting in BO while maintaining optimization performance comparable to baseline methods.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2504.07742 [stat.ML]

(or arXiv:2504.07742v4 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2504.07742 Focus to learn more arXiv-issued DOI via DataCite

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