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English(EN) The Zero Pattern of a Design Matrix Drives Multiple Descent in Over-parameterized Regression

新研究将协变量依赖性与回归中的多重下降联系起来

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新发表在arXiv上的论文探讨了过参数化线性回归，放宽了关于独立协变量和非退化协方差矩阵的常见假设。研究表明，退化和依赖性可能导致多重下降现象，并确定了方差变得奇异的特定配置。这些发现使用方差剖面的新颖图表示来表征，其中最大匹配和Dulmage--Mendelsohn分解确定了这些奇异方差峰值的位置。 AI

影响 为可能影响未来AI算法开发的回归模型提供了理论见解。

排序理由 该集群包含一篇发表在arXiv上的学术论文，详细介绍了统计学中的新颖数学发现。[lever_c_demoted from research: ic=1 ai=0.4]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新研究将协变量依赖性与回归中的多重下降联系起来

报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    The Zero Pattern of a Design Matrix Drives Multiple Descent in Over-parameterized Regression

    Over-parameterized linear regression has been widely studied over the last decade. However, most existing works assume that the covariates are independent and that their covariance matrices are non-degenerate. In this paper, we relax both assumptions and derive deterministic equi…

  2. arXiv stat.ML TIER_1 English(EN) · Kevin Han Huang, Haoyu Ye, Somak Laha, Morgane Austern ·

    设计矩阵的零模式驱动了过参数化回归中的多重下降

    arXiv:2607.24041v1 Announce Type: cross Abstract: Over-parameterized linear regression has been widely studied over the last decade. However, most existing works assume that the covariates are independent and that their covariance matrices are non-degenerate. In this paper, we re…

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