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English(EN) Exact Tail Laws for Nonlinear SGD with Infinite-Variance Gradients

新arXiv论文分析具有无限方差梯度的SGD

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的arXiv论文深入探讨了随机梯度下降（SGD）在处理无限方差梯度噪声时的数学基础。该研究通过分析非线性、恒定步长SGD的最后迭代的精确一阶尾部概率来表征SGD的准确性。研究表明，对于特定的梯度噪声特性，即使大多数迭代都接近最优值，SGD也可能表现出无限的预期超额目标，为理解这些极端行为提供了详细的数学框架。 AI

影响 提供了对训练大型AI模型至关重要的优化算法的更深层次的理论理解。

排序理由 该集群包含一篇在arXiv上发表的学术论文，详细介绍了优化算法的理论数学分析。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新arXiv论文分析具有无限方差梯度的SGD

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该集群包含一篇在arXiv上发表的学术论文，详细介绍了优化算法的理论数学分析。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Xiaoli Li, Qianqian Lei, Ziyang Wei, Wei Biao Wu ·

    非线性SGD的无限方差梯度精确尾部定律

    arXiv:2610.23750v1 Announce Type: cross Abstract: With infinite-variance gradient noise, SGD can have infinite expected excess objective even when most iterates are close to the optimum. We characterize its accuracy through exact first-order tail probabilities for the last iterat…

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