Poster
Lifted Weighted Mini-Bucket
Nicholas Gallo · Alexander Ihler

Thu Dec 6th 10:45 AM -- 12:45 PM @ Room 210 #28

Many graphical models, such as Markov Logic Networks (MLNs) with evidence, possess highly symmetric substructures but no exact symmetries. Unfortunately, there are few principled methods that exploit these symmetric substructures to perform efficient approximate inference. In this paper, we present a lifted variant of the Weighted Mini-Bucket elimination algorithm which provides a principled way to (i) exploit the highly symmetric substructure of MLN models, and (ii) incorporate high-order inference terms which are necessary for high quality approximate inference. Our method has significant control over the accuracy-time trade-off of the approximation, allowing us to generate any-time approximations. Experimental results demonstrate the utility of this class of approximations, especially in models with strong repulsive potentials.

Author Information

Nicholas Gallo (UC Irvine)
Alexander Ihler (UC Irvine)

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