Multi-task Learning of Order-Consistent Causal Graphs

no code implementations • NeurIPS 2021 • Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song

We consider the problem of discovering $K$ related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of supports.

Multi-Task Learning

NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters

1 code implementation • 1 Nov 2021 • Ben Lengerich, Caleb Ellington, Bryon Aragam, Eric P. Xing, Manolis Kellis

We encode the acyclicity constraint as a smooth regularization loss which is back-propagated to the mixing function; in this way, NOTMAD shares information between context-specific acyclic graphs, enabling the estimation of Bayesian network structures and parameters at even single-sample resolution.