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English(EN) Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise

新研究分析带噪声和裁剪的SGD收敛性 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了在挑战性条件下随机梯度下降（SGD）方法的收敛特性。第一篇论文分析了带梯度裁剪和加性高斯噪声的SGD，在平滑性和有界噪声假设下证明了几乎处处收敛性。第二篇论文研究了重尾噪声和Hölder平滑性下的SGD，为标准SGD、$\delta$-GClip和G-Clip建立了新的收敛速率，包括在非常重尾情况下的随机梯度方法的第一个保证。 AI

影响 这些理论分析可能导致更鲁棒、更高效的机器学习模型训练方法，尤其是在数据噪声大或目标复杂的情况下。

排序理由 两篇发表在arXiv上的学术论文，详细介绍了机器学习优化算法的理论进展。

在 arXiv cs.LG 阅读 →

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新研究分析带噪声和裁剪的SGD收敛性 · 跟踪2个来源

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两篇发表在arXiv上的学术论文，详细介绍了机器学习优化算法的理论进展。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Amartya Mukherjee, Jun Liu ·

    带裁剪和加性噪声的随机梯度方法的近乎确定收敛性分析

    arXiv:2609.12119v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) with gradient clipping and additive noise has become a standard technique for training machine learning models, particularly in applications requiring robustness or privacy guarantees. However, clip…

  2. arXiv cs.LG TIER_1 English(EN) · Misbah Uz Zaman, Anirbit Mukherjee ·

    重尾噪声和Hölder光滑性下随机梯度方法的收敛性

    arXiv:2609.12785v1 Announce Type: new Abstract: Classical convergence guarantees for stochastic gradient methods typically assume Lipschitz-smooth objectives and finite-variance gradient noise, both frequently violated in practice. In contrast, we study nonconvex stochastic optim…

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