English(EN) Sharp Generalization for Nonparametric Regression in Interpolation Space by Over-Parameterized Neural Networks Trained with Preconditioned Gradient Descent and Early Stopping

新的PGD算法提高了神经网络回归速率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一种新的预条件梯度下降（PGD）算法，用于训练非参数回归任务中的过参数化神经网络。该方法结合提前停止，实现了超越现有近乎最优速率的尖锐回归速率。该算法的有效性源于网络输出的新颖分解以及局部Rademacher复杂度理论在控制函数类别复杂度方面的应用。 AI

影响这项研究为训练神经网络提供了理论进步，可能带来更有效、更准确的回归模型。

排序理由详细介绍新算法及其理论保证的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Yingzhen Yang, Ping Li · 2026-10-27 04:00

过参数化神经网络通过预条件梯度下降和提前停止训练在插值空间中实现非参数回归的锐利泛化

arXiv:2407.11353v5 Announce Type: replace Abstract: We study nonparametric regression using an over-parameterized two-layer neural network trained with algorithmic guarantees in this paper. We consider the setting where the training features are drawn uniformly from the unit sphe…

本文如何被排名

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过参数化神经网络通过预条件梯度下降和提前停止训练在插值空间中实现非参数回归的锐利泛化

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