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新方法使用预取值(prequential e-values)为GP优化提供可审计的近最优性证书

研究人员开发了一种新方法,使用预取值(prequential e-values)为顺序黑盒函数优化中的近最优解创建可审计的证书。该方法解决了在认证结果之前,根据自适应评估调整高斯过程(GP)包络的常见做法,这种做法可能导致过度自信。通过用其自身的e-过程测试候选包络并消除被反驳的包络,该方法在特定条件下确保了随时有效性,显著降低了错误认证的风险,同时保持了效力。 AI

影响 这项研究为机器学习任务中的最优解认证提供了一种更可靠的方法,有可能改进超参数调整和模型选择过程。

排序理由 该集群包含一篇详细介绍优化证书新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新方法使用预取值(prequential e-values)为GP优化提供可审计的近最优性证书

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该集群包含一篇详细介绍优化证书新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ami Tavory, Noa Cohen ·

    Selected-GP的Prequential E-值近乎最优性证书

    arXiv:2609.39123v1 Announce Type: new Abstract: When optimizing an expensive black-box function sequentially, as in hyperparameter optimization, we may want to stop once the best evaluated value is certified within $\varepsilon$ of the global optimum. Such a certificate needs two…