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English(EN) Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization

新的tcGP方法改进了贝叶斯优化的高斯过程校准

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为tcGP的新方法，以改进高斯过程（GP）预测分布的校准，特别是关注低尾校准。这对于依赖这些分布来选择昂贵目标评估点的贝叶斯优化（BO）至关重要。所提出的框架解决了可能导致最小化任务中次优探索-利用权衡的失校准问题。实验表明，tcGP在标准基准测试中提高了BO算法的校准准确性和性能。 AI

影响 增强了贝叶斯优化的可靠性，可能导致在复杂系统中更有效的实验设计和超参数调优。

排序理由 发布了一篇详细介绍改进高斯过程校准新方法的学术论文。

在 arXiv stat.ML 阅读 →

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新的tcGP方法改进了贝叶斯优化的高斯过程校准

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Aur\'elien Pion, Emmanuel Vazquez ·

    高斯过程的面向目标下尾校准用于贝叶斯优化

    arXiv:2605.20145v1 Announce Type: new Abstract: Bayesian optimization (BO) selects evaluation points for expensive black-box objectives using Gaussian process (GP) predictive distributions. Kernel choice and hyperparameter selection can lead to miscalibrated predictive distributi…

  2. arXiv stat.ML TIER_1 English(EN) · Emmanuel Vazquez ·

    高斯过程的面向目标下尾校准用于贝叶斯优化

    Bayesian optimization (BO) selects evaluation points for expensive black-box objectives using Gaussian process (GP) predictive distributions. Kernel choice and hyperparameter selection can lead to miscalibrated predictive distributions and an inappropriate exploration-exploitatio…

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