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Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs

Mayukh Das; Devendra Singh Dhami; Gautam Kunapuli; Kristian Kersting; Sriraam Natarajan
In: The Thirty-Third AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence (AAAI-2019), Pages 7816-7824, AAAI Press, 2019.


Counting the number of true instances of a clause is arguably a major bottleneck in relational probabilistic inference and learning. We approximate counts in two steps:(1) transform the fully grounded relational model to a large hypergraph, and partially-instantiated clauses to hypergraph motifs;(2) since the expected counts of the motifs are provably the clause counts, approximate them using summary statistics (in/outdegrees, edge counts, etc). Our experimental results demonstrate the efficiency of these approximations, which can be applied to many complex statistical relational models, and can be significantly faster than state-of-the-art, both for inference and learning, without sacrificing effectiveness.

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