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.
From: Qiyu Wei [view email]
[v1]
Thu, 10 Apr 2025 13:38:15 UTC (6,769 KB)
[v2]
Fri, 16 May 2025 13:19:26 UTC (7,449 KB)
[v3]
Fri, 10 Oct 2025 12:32:32 UTC (7,915 KB)
[v4]
Wed, 26 Aug 2026 14:30:51 UTC (1,349 KB)
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