English(EN) Can SGD Handle Heavy-Tailed Noise?

新研究表明 SGD 对重尾噪声具有鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

一篇新论文研究了随机梯度下降 (SGD) 在面对重尾噪声（现代机器学习中的常见问题）时的理论能力。该研究为各种问题类别（包括凸、强凸和非凸目标）的原始 SGD 建立了收敛保证。这些发现表明，即使在噪声方差无界的场景中，SGD 仍然是一个鲁棒且理论上可靠的基准，挑战了先前对其局限性的假设。 AI

影响挑战了关于 SGD 局限性的假设，表明其在复杂学习环境中作为基准的持续相关性。

排序理由该集群包含一篇关于机器学习优化理论方面的同行评审学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Ilyas Fatkhullin, Florian H\"ubler, Guanghui Lan · 2026-10-05 04:00

SGD 能处理重尾噪声吗？

arXiv:2508.04860v2 Announce Type: replace-cross Abstract: Stochastic Gradient Descent (SGD) is a cornerstone of large-scale optimization, yet its theoretical behavior under heavy-tailed noise -- common in modern machine learning and reinforcement learning -- remains poorly unders…