Informed Initialization for Bayesian Optimization and Active Learning

• URL: http://arxiv.org/abs/2510.23681v1
• Date: Mon, 27 Oct 2025 15:05:12 GMT
• Title: Informed Initialization for Bayesian Optimization and Active Learning
• Authors: Carl Hvarfner, David Eriksson, Eytan Bakshy, Max Balandat,
• Abstract summary: We propose a novel acquisition strategy that balances predictive uncertainty reduction with hyperparameter learning using information-theoretic principles.<n>We demonstrate its effectiveness through extensive experiments in active learning and few-shot BO.
• Score: 13.105080815344174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian Optimization is a widely used method for optimizing expensive black-box functions, relying on probabilistic surrogate models such as Gaussian Processes. The quality of the surrogate model is crucial for good optimization performance, especially in the few-shot setting where only a small number of batches of points can be evaluated. In this setting, the initialization plays a critical role in shaping the surrogate's predictive quality and guiding subsequent optimization. Despite this, practitioners typically rely on (quasi-)random designs to cover the input space. However, such approaches neglect two key factors: (a) space-filling designs may not be desirable to reduce predictive uncertainty, and (b) efficient hyperparameter learning during initialization is essential for high-quality prediction, which may conflict with space-filling designs. To address these limitations, we propose Hyperparameter-Informed Predictive Exploration (HIPE), a novel acquisition strategy that balances predictive uncertainty reduction with hyperparameter learning using information-theoretic principles. We derive a closed-form expression for HIPE in the Gaussian Process setting and demonstrate its effectiveness through extensive experiments in active learning and few-shot BO. Our results show that HIPE outperforms standard initialization strategies in terms of predictive accuracy, hyperparameter identification, and subsequent optimization performance, particularly in large-batch, few-shot settings relevant to many real-world Bayesian Optimization applications.

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