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English(EN) Non-Parametric Optimization for Scalable Learning in Stochastic Decision Problems

新的非参数框架增强了随机优化中的可扩展学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为随机优化（SO）问题开发了一个新颖的非参数框架，特别解决了对自适应控制和机器学习至关重要的时变场景。这种基于Malliavin微积分的新方法，为具有随机决策的SO建立了非参数最优性条件。它能够创建一个名为随机路径跟随器（SPF）的可扩展深度学习算法，该算法在参数化维度上表现出鲁棒性，并在分布漂移下的最小二乘恢复和逻辑回归任务中优于现有方法。 AI

影响 这项研究引入了一种在不确定性下更具可扩展性和鲁棒性的机器学习方法，有可能改善自适应控制和各种ML应用中的决策。

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

在 arXiv cs.LG 阅读 →

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新的非参数框架增强了随机优化中的可扩展学习

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该集群包含一篇详细介绍新算法框架及其应用的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mohsen Amidzadeh, Lauri Viitasaari, Mario Di Francesco ·

    面向随机决策问题可扩展学习的非参数优化

    arXiv:2610.23204v1 Announce Type: new Abstract: Stochastic optimization (SO) plays a central role in decision-making under uncertainty. Among SO problems, time-varying stochastic optimization (TV-SO) is particularly important due to its applications in adaptive control and machin…

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