Differentiable and Transportable Structure Learning. (arXiv:2206.06354v2 [cs.LG] UPDATED)

Directed acyclic graphs (DAGs) encode a lot of information about a particular
distribution in its structure. However, compute required to infer these
structures is typically super-exponential in the number of variables, as
inference requires a sweep of a combinatorially large space of potential
structures. That is, until recent advances made it possible to search this
space using a differentiable metric, drastically reducing search time. While
this technique -- named NOTEARS -- is widely considered a seminal work in
DAG-discovery, it …

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