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GP-UCB algorithm's suboptimality revealed in new research

By PulseAugur Editorial · [1 sources] ·

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

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GP-UCB algorithm's suboptimality revealed in new research

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

  1. arXiv stat.ML TIER_1 English(EN) · Wenjia Wang, Xiaowei Zhang ·

    On the Suboptimality of GP-UCB under Polynomial Effective Optimism

    arXiv:2312.01386v2 Announce Type: replace-cross Abstract: Gaussian process upper confidence bound (GP-UCB) is widely used for sequential optimization of expensive black-box functions. Although many upper bounds on its cumulative regret have been established in the literature, whe…

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