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English(EN) The Exact Worst-Case Tail Probability under Bounded Kurtosis

新论文精确描绘有界峰度下的尾部概率

研究人员确定了具有有界峰度的随机变量的精确最坏情况尾部概率。该分析定义了一个四区域图,详细说明了峰度界如何影响单侧尾部控制,揭示了四阶矩的信息可以抵消二阶矩界提供的改进。研究结果还确定了这些界所需的最小平方和证明程度,并提供了明确的对偶证书和极值分布。 AI

影响 这项研究提供了一个理论框架,可以为AI算法的设计和分析提供信息,特别是在理解其在不确定性下的输出的鲁棒性和可预测性方面。

排序理由 该集群包含一篇在arXiv上发表的学术论文,详细介绍了一项新的数学发现。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新论文精确描绘有界峰度下的尾部概率

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该集群包含一篇在arXiv上发表的学术论文,详细介绍了一项新的数学发现。
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报道来源 [3]

  1. arXiv stat.ML TIER_1 English(EN) · Xiaoyu Li, Andi Han, Jiaojiao Jiang, Junbin Gao ·

    有界峰度下的最坏情况尾部概率精确值

    arXiv:2607.05226v1 Announce Type: cross Abstract: We determine exactly what a kurtosis bound buys for one-sided tail control. For the class $\mathcal{C}(\kappa)$ of real random variables with mean $0$, variance $1$, and fourth moment at most $\kappa$, the skewness left free, we c…

  2. arXiv stat.ML TIER_1 English(EN) · Junbin Gao ·

    有界峰度下的精确最坏情况尾部概率

    We determine exactly what a kurtosis bound buys for one-sided tail control. For the class $\mathcal{C}(κ)$ of real random variables with mean $0$, variance $1$, and fourth moment at most $κ$, the skewness left free, we compute the worst-case tail probability $V_1(t,κ)=\sup_{X\in\…

  3. arXiv stat.ML TIER_1 English(EN) · Junbin Gao ·

    有界峰度下的精确最坏情况尾部概率

    We determine exactly what a kurtosis bound buys for one-sided tail control. For the class $\mathcal{C}(κ)$ of real random variables with mean $0$, variance $1$, and fourth moment at most $κ$, the skewness left free, we compute the worst-case tail probability $V_1(t,κ)=\sup_{X\in\…