GP-UCB algorithm achieves nearly optimal regret in noise-free bandits

A new paper on arXiv introduces a theoretical analysis of the Gaussian Process Upper Confidence Bound (GP-UCB) algorithm for noise-free Gaussian Process bandits. The research demonstrates that GP-UCB achieves a nearly optimal regret upper bound, resolving a discrepancy between its strong empirical performance and previous theoretical indications of suboptimality. The findings specifically show a constant cumulative regret for common kernels like the squared exponential and Matérn kernels in noise-free settings. AI

IMPACT Provides theoretical justification for the effectiveness of GP-UCB in bandit problems, potentially guiding future research in reinforcement learning and decision-making under uncertainty.

RANK_REASON The cluster contains an academic paper detailing theoretical advancements in an AI-related algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Gaussian Process (GP) bandits
• Gaussian Process Upper Confidence Bound
• GP-UCB
• Matérn kernel
• reproducing kernel Hilbert space
• Shogo Iwazaki
• Squared exponential kernel

• paper
• other

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