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Differentiable and Transportable Structure Learning
Jeroen Berrevoets · Nabeel Seedat · Fergus Imrie · Mihaela van der Schaar

Tue Jul 25 02:00 PM -- 04:30 PM (PDT) @ Exhibit Hall 1 #128

Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their 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 concedes an important property in favour of differentiability: transportability. To be transportable, the structures discovered on one dataset must apply to another dataset from the same domain. We introduce D-Struct which recovers transportability in the discovered structures through a novel architecture and loss function while remaining fully differentiable. Because D-Struct remains differentiable, our method can be easily adopted in existing differentiable architectures, as was previously done with NOTEARS. In our experiments, we empirically validate D-Struct with respect to edge accuracy and structural Hamming distance in a variety of settings.

Author Information

Jeroen Berrevoets (University of Cambridge)
Nabeel Seedat (University of Cambridge)
Fergus Imrie (University of California, Los Angeles)
Mihaela van der Schaar (University of Cambridge and UCLA)

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