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English(EN) On the Accuracy of Approximate Leave-One-Out with Blockwise Nonsmooth Regularization in High-dimensions

新论文为近似留一法风险估计提供理论支持

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新近发表在arXiv上的论文介绍了一种近似留一法（ALO）风险估计的启发式方法，该方法专门用于高维统计问题中的非光滑正则化。该研究为这种启发式方法提供了理论依据，证明了精确留一法和近似留一法损失之间的差异有界，其界限为 $O(\log^{3/2}(en)/\sqrt{n})$。该分析对训练数据受到扰动时稀疏估计器的稳定性提供了新的见解，并在比先前工作更弱的假设下加强了保证。 AI

影响 为与机器学习遗忘和数据估值相关的技术提供了理论基础。

排序理由 发表在arXiv上的学术论文，详细介绍了一种新的统计方法。[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 →

新论文为近似留一法风险估计提供理论支持

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发表在arXiv上的学术论文，详细介绍了一种新的统计方法。[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) · Honghao Lin, Songbin Liu, Arian Maleki, Vahab Mirrokni, Chen Shang, David P. Woodruff, Hanbo Yang, Wenjin Zhang ·

    高维情况下块状非光滑正则化近似留一法准确性研究

    arXiv:2610.27502v1 Announce Type: cross Abstract: Approximate leave-one-out (ALO) refers to methods that provide computationally efficient and statistically accurate approximations to leave-one-out risk estimation. These methods have recently attracted increased attention because…

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