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English(EN) Simultaneous analysis of approximate leave-one-out cross-validation and mean-field inference

新分析证明ALO-CV对高维估计量的一致性

作者 PulseAugur 编辑部 · [1 个来源] ·

对近似留一交叉验证（ALO-CV）的新分析已被开发出来，以证明其在高斯协变量和强凸性下对不可微正则化器的 समरूपता。该方法旨在估计高维设置中正则化估计器的泛化误差，其中样本量和维度相当。研究表明，ALO-CV权重与均场推断的对应项非常接近，从而证明了ALO-CV以可忽略的误差准确估计了留一量。 AI

影响 这项研究为评估高维场景下机器学习模型的泛化提供了一个理论框架。

排序理由 该集群包含一篇关于新统计分析方法的arXiv预印本。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新分析证明ALO-CV对高维估计量的一致性

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该集群包含一篇关于新统计分析方法的arXiv预印本。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Pierre C Bellec ·

    近似留一法交叉验证与均场推断的同步分析

    arXiv:2501.02624v3 Announce Type: replace-cross Abstract: Approximate Leave-One-Out Cross-Validation (ALO-CV) is a method that has been proposed to estimate the generalization error of a regularized estimator in the high-dimensional regime where dimension and sample size are of t…

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