English(EN) Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders

新方法改进了复杂模型中的因果结构学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种用于学习复杂系统因果结构的新方法，特别关注包含有向循环和潜在混淆因素的线性高斯模型。该方法通过最小化高斯负对数似然并对模型复杂度进行惩罚来实现，该惩罚考虑了边和潜在变量。该方法使用伯努利门来参数化这些元素的包含，从而能够对结构系数和概率进行连续优化。实验表明，该技术在准确恢复因果结构方面优于以前的方法。 AI

影响引入了一种新颖的因果推理方法，有可能提高AI理解复杂系统的能力。

排序理由详细介绍因果结构学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Sadegh Khorasani, Ali Najar, Saber Salehkaleybar, Negar Kiyavash · 2026-10-01 04:00

具有潜在混淆变量的循环线性高斯模型的微分结构学习

arXiv:2609.38618v1 Announce Type: cross Abstract: We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unknown number of exogenous latent confounders, bounded by a given maximum. We derive…

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报道来源 [1]

具有潜在混淆变量的循环线性高斯模型的微分结构学习

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