arXiv:2502.01226v4 Announce Type: replace Abstract: Gaussian process (GP) bandits provide a powerful framework for performing blackbox optimization of unknown functions. The characteristics of the unknown function depend heavily on the assumed GP prior. Most work in the literature assume that this prior is known but in practice this seldom holds. Instead, practitioners often rely on maximum likelihood estimation to select the hyperparameters of the prior - which lacks theoretical guarantees. In this work, we study two algorithms for joint prior selection and regret minimization in GP bandits b
This research addresses a long-standing theoretical gap in Gaussian Process (GP) bandits concerning prior selection, moving towards more robust and theoretically sound black-box optimization.
Improved GP bandit performance with theoretical guarantees for prior selection can significantly enhance the efficiency and reliability of AI agents and automated decision-making systems in various applications.
The ability to dynamically adapt prior selection in GP bandits means a more flexible and less assumption-dependent approach to sequential decision-making and optimization, reducing the need for manual tuning.
- · AI researchers and developers
- · Companies using AI for optimization
- · Autonomous systems developers
- · Systems reliant on sub-optimal GP prior selection
- · Manual hyperparameter tuners
More efficient and reliable black-box optimization in AI-driven systems.
Accelerated development and practical deployment of AI agents that learn and adapt with less human intervention.
Enhanced automation across industries, potentially contributing to the broader adoption of AI agents in complex decision-making scenarios.
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Read at arXiv cs.LG