Effective Causal Discovery under Identifiable Heteroscedastic Noise Model

Capturing the underlying structural causal relations represented by Directed Acyclic Graphs (DAGs) has been a fundamental task in various AI disciplines. Causal DAG learning via the continuous optimization framework has recently achieved promising performance in terms of both accuracy and efficiency...

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Veröffentlicht in:	arXiv.org 2024-06
Hauptverfasser:	Yin, Naiyu, Gao, Tian, Yu, Yue, Ji, Qiang
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Data collection Graph theory Graphical representations Iterative methods Machine learning Noise Optimization Synthetic data Variance
Online-Zugang:	Volltext
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