English(EN) Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent

研究人员开发了用于SGD置信集的_novel_bootstrap方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

研究人员开发了一种用于构建随机梯度下降（SGD）算法中置信集的新颖方法。这种新方法利用了乘数自举程序并建立了其_non-asymptotic_有效性。该方法实现了$1/\sqrt{n}$阶的近似率，这可能比现有的中心极限定理方法更快，并提供了SGD中自举准确性的第一个完全_non-asymptotic_界限。 AI

影响引入了一种新颖的统计技术，可以提高机器学习模型训练的可靠性。

排序理由这是一篇详细介绍机器学习算法新统计方法的_research paper_。 [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) · Marina Sheshukova, Sergey Samsonov, Denis Belomestny, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov · 2026-05-05 04:00

随机梯度下降的高斯近似与乘数自举法

arXiv:2502.06719v3 Announce Type: replace Abstract: In this paper, we establish the non-asymptotic validity of the multiplier bootstrap procedure for constructing the confidence sets using the Stochastic Gradient Descent (SGD) algorithm. Under appropriate regularity conditions, o…

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报道来源 [1]

随机梯度下降的高斯近似与乘数自举法

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