BOOST: A Data-Driven Framework for the Automated Joint Selection of Kernel and Acquisition Functions in Bayesian Optimization
arXiv:2508.02332v4 Announce Type: replace Abstract: The performance of Bayesian optimization (BO), a highly sample-efficient method for expensive black-box problems, is critically governed by the selection of its hyperparameters, including the kernel and acquisition functions. This presents a significant practical challenge: an inappropriate combination of these can lead to poor performance and wasted evaluations. While individual improvements to kernel functions and acquisition functions have been actively explored, the joint and autonomous selection of the best pair of these fundamental hype
The increasing complexity of AI model training and optimization demands more sophisticated and autonomous methods for hyperparameter selection to maintain performance gains.
Automated and optimal selection of Bayesian optimization hyperparameters directly improves the efficiency and effectiveness of crucial AI development processes, impacting R&D speed and resource allocation.
The reliance on manual expert tuning for critical AI optimization components will decrease, enabling faster and more robust development cycles for complex AI systems.
- · AI/ML researchers
- · Companies with high-cost AI experiments
- · Cloud AI platforms
- · Manual hyperparameter tuning experts
More efficient and less resource-intensive development of new AI models and applications.
Accelerated innovation in areas utilizing Bayesian optimization, potentially including drug discovery and materials science.
Enhanced overall AI capabilities due to more robust and autonomously optimized foundational algorithms.
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