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Poster
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
in Poster Session 1 »

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.

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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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