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English(EN) Regularization vs. Convolution in Kernel-Based Hypothesis Testing

基于核的假设检验探索正则化与卷积

本文探讨了基于核的假设检验,特别是最大均值差异(MMD)检验。研究了两种不同的方法:一种用于 $\mathbb{R}^d$ 上平移不变核的卷积方法,以及一种用于一般可测量空间的正则化方法。研究通过证明在满足核谱密度特定条件的情况下,两种方法在 $L^2$-分离的 Sobolev 球上都能达到 minimax 最优性,从而将它们联系起来。研究表明,对于 Matérn 和 Laplace 等特定核,具有消失的正则化参数的固定带宽可以达到 Sobolev minimax 速率。 AI

排序理由 该项目是一篇学术论文,详细介绍了统计方法的理论进展。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv stat.ML 阅读 →

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基于核的假设检验探索正则化与卷积

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该项目是一篇学术论文,详细介绍了统计方法的理论进展。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Omar Hagrass, Bharath K. Sriperumbudur ·

    正则化与卷积在基于核的假设检验中的应用

    arXiv:2611.06778v1 Announce Type: cross Abstract: The maximum mean discrepancy (MMD) test is a popular nonparametric test on general domains. Two lines of work have studied its optimality through structurally different approaches. The first, a convolution approach on $\mathbb{R}^…