新的理论框架解释了过参数化学习中的泛化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-21 04:00

一篇题为“Spectral-Transport Stability and Benign Overfitting in Interpolating Learning”的研究论文发表在arXiv上，该论文提出了一个理论框架来理解高度过参数化学习模型中的泛化。该论文提出了一种“谱传输稳定性”方法来控制过剩风险，并将其与数据几何、学习规则敏感性和标签噪声联系起来。它引入了一个“Fredriksson指数”来表征复杂性并建立良性过拟合的标准，并为多项式谱线性插值推导了明确的速率。 AI

影响为理解机器学习模型的泛化提供了一个理论框架，可能指导未来的模型开发。

排序理由发表在arXiv上的学术论文。[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) · Gustav Olaf Yunus Laitinen-Lundstr\"om Fredriksson-Imanov · 2026-07-21 04:00

谱传输稳定性与插值学习中的良性过拟合

arXiv:2604.08625v2 Announce Type: replace Abstract: We develop a theoretical framework for generalization in the interpolating regime of statistical learning. The central question is why highly overparameterized estimators can attain zero empirical risk while still achieving nont…

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谱传输稳定性与插值学习中的良性过拟合

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