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新的自适应停止规则提高了随机优化中 SGD 的效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为随机梯度下降 (SGD) 等随机优化算法开发了新的轨迹自适应停止规则。这些规则解决了理论固定时间分析与实际自适应停止决策之间的不匹配问题，确保了统计有效性。该方法使用置信上限序列来界定优化误差和次优性，实现了最优衰减率并适应实际梯度。这种方法允许 SGD 一旦达到所需的精度即可高效停止，可能比传统的确定性时间范围需要更少的迭代次数，并且已扩展到小批量 SGD。 AI

影响 提高了用于训练机器学习模型的优化算法的效率。

排序理由 该集群包含一篇详细介绍随机优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新的自适应停止规则提高了随机优化中 SGD 的效率

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该集群包含一篇详细介绍随机优化新方法的学术论文。[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) · Liviu Aolaritei, Lucas L\'evy, Francis Bach, Michael I. Jordan ·

    Beyond Optimal Rates in Stochastic Optimization: Trajectory-Adaptive Stopping Rules

    arXiv:2608.25551v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) is typically analyzed at a deterministic horizon chosen before the algorithm is run, even though practical stopping decisions are made adaptively by inspecting the evolving trajectory. This mismat…

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