arXiv:2603.09276v2 Announce Type: replace-cross Abstract: We study a widely used Bayesian optimization method, Gaussian process Thompson sampling (GP-TS), under the assumption that the objective function is a sample path from a GP. Compared with the GP upper confidence bound (GP-UCB) with established high-probability and expected regret bounds, most analyses of GP-TS have been limited to expected regret. Moreover, whether the recent analyses of GP-UCB for the lenient regret and the improved cumulative regret upper bound can be applied to GP-TS remains unclear. To fill these gaps, this paper sh
This research continues the ongoing effort to improve the theoretical understanding and practical efficiency of core algorithms in artificial intelligence, with specific relevance to automated decision-making and experimentation.
Improved regret bounds for Bayesian optimization algorithms enhance the reliability and efficiency of AI systems that learn and optimize in complex environments, impacting fields from drug discovery to autonomous systems.
The theoretical foundation for Gaussian process Thompson sampling is strengthened, potentially leading to more robust and predictable performance in real-world applications compared to prior understanding.
- · AI researchers
- · Machine learning practitioners
- · Drug discovery sector
- · Autonomous systems developers
More efficient and reliable AI-driven optimization in various research and industrial applications.
Accelerated development cycles for products and services relying on efficient experimentation and black-box optimization.
Reduced computational costs and resource waste in complex optimization problems, contributing to broader AI accessibility.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG