GP-UCB algorithm's suboptimality revealed in new research

A new paper published on arXiv investigates the limitations of the Gaussian Process Upper Confidence Bound (GP-UCB) algorithm. Researchers have established upper bounds on its cumulative regret, but this work explores whether GP-UCB is truly minimax optimal. The study introduces a new regret lower bound for GP-UCB with Matérn kernels, indicating that polynomial growth in the effective optimism level hinders optimal regret rates. AI

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IMPACT Identifies a fundamental limitation in a widely used optimization algorithm, potentially guiding future research towards more optimal methods.

RANK_REASON The cluster contains an academic paper discussing algorithmic limitations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Gaussian Process Upper Confidence Bound
• GP-UCB
• Matérn kernels
• Xiaowei Zhang

• paper
• other