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新研究详解了用于依赖观测的随机镜像下降法

一篇新研究论文探讨了随机镜像下降(SMD)算法,重点关注其在处理依赖观测而非典型的独立同分布(i.i.d.)数据时的收敛性质。该研究引入了一种新颖的“遗忘”条件来处理具有几何或多项式依赖性的序列。该框架为平均SMD迭代给出了明确的有限样本精度界限,在不同的误差范围内实现了标准速率。该论文展示了这种通用方法在高维线性回归(具有依赖性回归量)和函数自回归模型的非参数估计中的适用性。 AI

影响 这项研究可以提高机器学习优化算法在处理现实世界非i.i.d.数据时的鲁棒性。

排序理由 该集群包含一篇详细介绍新算法及其应用的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新研究详解了用于依赖观测的随机镜像下降法

本文如何被排名

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Tool
该集群包含一篇详细介绍新算法及其应用的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv stat.ML TIER_1 Deutsch(DE) · Gersende Fort, Anatoli Juditsky, Jiaming Liang ·

    Stochastic Mirror Descent under Dependent Observations

    arXiv:2610.41251v1 Announce Type: cross Abstract: We study the non-asymptotic convergence of Stochastic Mirror Descent (SMD) algorithm when the noisy first-order oracle is driven by dependent observations. Departing from the classical i.i.d. assumption underlying most existing gu…