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English(EN) On the Suboptimality of GP-UCB under Polynomial Effective Optimism

GP-UCB算法的次优性在新研究中揭示

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的论文研究了高斯过程上限置信界（GP-UCB）算法的局限性。研究人员已经确定了其累积遗憾的上界，但这项工作探讨了GP-UCB是否真正具有minimax最优性。该研究为具有Matérn核的GP-UCB引入了一个新的遗憾下界，表明有效乐观度水平的多项式增长阻碍了最优遗憾率。 AI

影响 识别出一种广泛使用的优化算法的基本局限性，可能指导未来研究朝着更优化的方法发展。

排序理由 该集群包含一篇讨论算法局限性的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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GP-UCB算法的次优性在新研究中揭示

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报道来源 [1]

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

    GP-UCB在多项式有效乐观下的次优性分析

    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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