English(EN) Gaussian Process Upper Confidence Bound Achieves Nearly Optimal Regret in Noise-Free Gaussian Process Bandits

GP-UCB算法在无噪声老虎机中实现近乎最优遗憾

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

arXiv上的一篇新论文介绍了对无噪声高斯过程老虎机的高斯过程置信上界（GP-UCB）算法的理论分析。研究表明，GP-UCB实现了近乎最优的遗憾上界，解决了其强大的经验性能与先前理论上显示次优性之间的差异。研究结果特别显示，在无噪声环境下，对于平方指数核和Matérn核等常见核，累积遗憾为常数。 AI

影响为GP-UCB在老虎机问题中的有效性提供了理论依据，可能指导未来在强化学习和不确定性下的决策研究。

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arXiv cs.LG TIER_1 English(EN) · Shogo Iwazaki · 2026-11-02 05:00

Gaussian Process Upper Confidence Bound Achieves Nearly Optimal Regret in Noise-Free Gaussian Process Bandits

arXiv:2502.19006v3 Announce Type: replace Abstract: We study the noise-free Gaussian Process (GP) bandits problem, in which the learner seeks to minimize regret through noise-free observations of the black-box objective function lying on the known reproducing kernel Hilbert space…

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Gaussian Process Upper Confidence Bound Achieves Nearly Optimal Regret in Noise-Free Gaussian Process Bandits

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