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.
Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables,
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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.