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Structure Learning for Bayesian network (BN) is an important problem with extensive research.
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Perron-frobenius theorem for nonnegative tensors
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Nonlinear causal discovery with additive noise models
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Firefly algorithm for continuous constrained optimization tasks
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Directlingam: A direct method for learning a linear non-gaussian structural equation model
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Spectral Radius
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Elements of causal inference: foundations and learning algorithms
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A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images
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Learning functional causal models with generative neural networks
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Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
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Penalized estimation of directed acyclic graphs from discrete data
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Gradient-based neural dag learning
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Scaling structural learning with no-bears to infer causal transcriptome networks
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A survey on bayesian network structure learning from data
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Dag-gnn: Dag structure learning with graph neural networks
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Learning sparse nonparametric dags
Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, and Eric P Xing · 2019
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Bn learning repository
Marco Scutari · 2020
Closest in time.