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English(EN) Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation

新理论将高斯普适性和CGMT扩展到依赖数据设置

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员已将高斯普适性和凸高斯极小极大定理（CGMT）的原理扩展到依赖数据设置。这项工作表明，即使存在块依赖、m依赖和某些混合条件，高斯普适性仍适用于高维逻辑回归。此外，已开发了一个新的CGMT框架来解释协变量和观测值中的相关性。这些进展有助于更好地理解数据增强实践对深度学习中渐近风险的影响。 AI

影响 为理解依赖数据条件下深度学习中的数据增强提供了理论基础。

排序理由 该集群包含一篇详细介绍统计学理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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 →

新理论将高斯普适性和CGMT扩展到依赖数据设置

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该集群包含一篇详细介绍统计学理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Matthew Esmaili Mallory, Kevin Han Huang, Morgane Austern ·

    高维逻辑回归的普适性及一种新颖的依赖性CGMT及其在数据增强中的应用

    arXiv:2502.15752v4 Announce Type: replace-cross Abstract: Over the last decade, a wave of research has characterized the exact asymptotic risk of many high-dimensional models in the proportional regime. Two foundational results have driven this progress: Gaussian universality, wh…

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