English(EN) When Is the Sharp Covariance Envelope Tight? Feature-Only Geometry for Volume-Sampled Least Squares

面向体积采样最小二乘的新几何方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

本文深入探讨了统计机器学习中协方差矩阵的几何性质，特别关注体积采样最小二乘。研究人员 Derezinski 和 Warmuth 建立了基础采样恒等式和无偏性性质。当前工作通过定义系数协方差的尖锐 Loewner 包络来扩展这一领域，该包络被证明在各种条件下全局尖锐。本文还引入了一个仅特征的几何条件，该条件确定了精确的光谱相位，表明在兼容残差下，光谱包络何时是严格或收紧的。 AI

影响为体积采样最小二乘的协方差分析引入了新的几何见解，可能改进某些机器学习算法的理论理解。

排序理由这是一篇发表在 arXiv 上的研究论文，详细介绍了统计机器学习方面的理论进展。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Kihun Rhee · 2026-08-28 04:00

Sharp协方差包络何时收紧？面向体积采样最小二乘的仅特征几何

arXiv:2608.26877v1 Announce Type: cross Abstract: Prior analyses by Derezinski and Warmuth established all-size sampling identities, selected-OLS unbiasedness, and inverse moments for ordinary volume sampling, while their exact arbitrary-fixed-response loss and prediction-covaria…

本文如何被排名

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Sharp协方差包络何时收紧？面向体积采样最小二乘的仅特征几何

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