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English(EN) Dimension-free regimes for unadjusted Langevin in ridge-separable models: A subspace robust Wasserstein analysis

对未调整Langevin算法的新分析提供了无量纲见解

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的论文介绍了一种针对未调整Langevin算法（ULA）的子空间鲁棒Wasserstein分析。该方法旨在为高维贝叶斯推断提供更准确的偏差和混合时间预测，尤其是在过参数化模型中。分析揭示了一个取决于数据点数量相对于模型维度的钟形混合剖面，这表明无量纲保证并非普遍可实现。 AI

影响 为贝叶斯推断算法提供了更细致的理论理解，有可能改善其在高维环境中的应用。

排序理由 该集群包含一篇在arXiv上发表的学术论文，详细介绍了对一种机器学习算法的新理论分析。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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对未调整Langevin算法的新分析提供了无量纲见解

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该集群包含一篇在arXiv上发表的学术论文，详细介绍了对一种机器学习算法的新理论分析。[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) · Yuansi Chen, Francesco Pedrotti, Peter A. Whalley ·

    Dimension-free regimes for unadjusted Langevin in ridge-separable models: A subspace robust Wasserstein analysis

    arXiv:2610.31431v1 Announce Type: cross Abstract: The Unadjusted Langevin Algorithm (ULA) is widely used for high-dimensional Bayesian inference. Classical analyses in Wasserstein-2 distance predict an asymptotic bias and a mixing time that grow with dimension, but this metric is…

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