English(EN) DAG Structure Learning with Node-Varying Heavy-Tailed Disturbances

新的DAGMA方法改进了具有变化扰动的结构学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了一种从数据中学习有向无环图（DAG）结构的新方法，特别解决了跨节点尺度和尾部行为变化的扰动带来的挑战。提出的节点变化学生t分布DAGMA模型联合估计图结构、单个节点扰动尺度和自由度参数。该方法利用高斯尺度混合表示来实现迭代重加权公式，有效降低了大的残差权重，从而在具有异质重尾扰动的合成实验中提高了结构恢复能力。实际金融数据实验表明，与现有基线相比，该方法实现了更低的测试负对数似然。 AI

影响在复杂非高斯数据分布存在的情况下，增强了因果发现方法的鲁棒性。

排序理由关于DAG结构学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Robin Francis, Sundeep Prabhakar Chepuri · 2026-10-12 04:00

带节点变化重尾扰动的DAG结构学习

arXiv:2610.13304v1 Announce Type: new Abstract: DAG structures can be learned from observations by incorporating acyclicity via smooth constraints, such as DAGMA's log-determinant function. Typically, observations and the underlying DAG are related through linear structural equat…

本文如何被排名

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带节点变化重尾扰动的DAG结构学习

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