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English(EN) On McDiarmid's Inequality under Dependence via Approximate Tensorization of Entropy

新论文通过熵的近似张量化探讨McDiarmid不等式

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员发表了一篇论文，详细介绍了McDiarmid不等式的进展，该不等式可应用于统计学、学习理论和理论计算机科学。该工作强调了熵的近似张量化（ATE）如何蕴含McDiarmid不等式，并推导了非各向同性高斯随机向量的一个版本。研究结果还将集中不等式推广到强对数凹和对数光滑概率测度，改进了先前关于非独立同分布观测的结果。 AI

排序理由 该集群包含一篇在arXiv上发表的研究论文，详细介绍了数学统计学和学习理论方面的理论进展。

在 arXiv stat.ML 阅读 →

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新论文通过熵的近似张量化探讨McDiarmid不等式

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该集群包含一篇在arXiv上发表的研究论文，详细介绍了数学统计学和学习理论方面的理论进展。
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Valentin Roth ·

    关于依赖下McDiarmid不等式通过熵的近似张量化

    arXiv:2606.12720v1 Announce Type: cross Abstract: We argue that dependent versions of McDiarmid's inequality are a useful but underutilized tool in mathematical statistics, learning theory and theoretical computer science. To make this point, we first highlight that approximate t…

  2. arXiv stat.ML TIER_1 English(EN) · Valentin Roth ·

    关于依赖下McDiarmid不等式通过熵的近似张量化

    We argue that dependent versions of McDiarmid's inequality are a useful but underutilized tool in mathematical statistics, learning theory and theoretical computer science. To make this point, we first highlight that approximate tensorization of entropy (ATE) implies McDiarmid's …

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