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English(EN) Variational Tail Bounds for Norms of Random Vectors and Matrices

新研究引入随机向量和矩阵范数的变分尾部界限

一篇新研究论文引入了随机向量和矩阵范数的变分尾部界限,提供了一种在特定矩假设下分析这些数量的方法。该论文详细介绍了一种使用高斯分布推前(pushforward)的简化界限,并将该方法应用于推导随机向量欧几里得范数的无量纲尾部界限。此外,它重现了正半定矩阵和随机矩阵序列的现有集中不等式,并为样本协方差矩阵和随机矩阵序列建立了新的不等式。 AI

影响 提供了可能为开发更鲁棒、更高效的机器学习算法提供理论工具。

排序理由 该集群包含一篇在 arXiv 上发表的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新研究引入随机向量和矩阵范数的变分尾部界限

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该集群包含一篇在 arXiv 上发表的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Sohail Bahmani ·

    随机向量和矩阵范数的变分尾部界限

    arXiv:2503.17300v5 Announce Type: replace-cross Abstract: We propose a variational tail bound for norms of random vectors and matrices under moment assumptions on their one-dimensional marginals. A simplified version of the bound that parametrizes the ``aggregating distribution''…