The paper introduces a non-negative DAG learning formulation solved via method of multipliers, with proofs that the true DAG is the unique global minimizer and only acyclic KKT point in the population regime.
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A tutorial on score-based DAG learning methods that jointly estimate sparse structure and heteroscedastic noise for improved robustness.
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Exploiting Non-Negativity in DAG Structure Learning
The paper introduces a non-negative DAG learning formulation solved via method of multipliers, with proofs that the true DAG is the unique global minimizer and only acyclic KKT point in the population regime.
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Concomitant DAG Learning: On the Roles of Noise Adaptivity, Sparsity, and Non-negativity
A tutorial on score-based DAG learning methods that jointly estimate sparse structure and heteroscedastic noise for improved robustness.