Lifted First-Order Probabilistic Inference

By Rodrigo de Salvo Braz, Eyal Amir, Dan Roth. International Joint Conference on Artificial Intelligence, 2005

373 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dae7f7b4819c13a0adf23afc29eb0870ec8e2e16

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In Artificial Intelligence

Citations in the corpus

Markov Chains on Orbits of Permutation Groups (2012, background): “A considerable amount of attention to approaches utilizing model symmetries has been given by work on “lifted probabilistic inference [36, 9].””
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, background): “See de Salvo Braz et al. (2007), Kersting (2012), and Kimmig et al. (2015) for more information on lifted inference.”
Open-Universe Weighted Model Counting (2017, background): “…propositional resolution steps, there are a number of extensions to WMC that leverage the first-order structure of template models by lumping together classes of objects (Poole 2003; de Salvo Braz, Amir, and Roth 2005; Gogate and Domingos 2011; Beame et al. 2015), referred to as lifted inference.”
Counting Belief Propagation (2009, methodology): “Specifically, we apply counting BP to inference for dynamic relational probabilistic models and to model counting for Boolean formulas.”
Lifted First-Order Belief Propagation (2008, methodology): “A better option is to use a more compact intensional representation , as done by Poole (2003) and Braz et al. (2005; 2006).”
Automorphism Groups of Graphical Models and Lifted Variational Inference (2012, background): “A more recent approach known as lifted inference [4, 16, 7, 8] has demonstrated the possibility to perform very efficient inference in highly-connected, but symmetric models, such as those arising in the context of relational (first-order) probabilistic models.”
Weighted Model Counting With Function Symbols (2017, neutral): “Using the relational structure to speed-up inference has also received attention [Gogate and Domingos, 2011, de Salvo Braz et al., 2005 Recall that functions can simulate predicates easily.”
Introduction to statistical relational learning (2007, background): “Generally, under counting elimination, choices for one atom cannot constrain the choices for another atom (there are exceptions to this rule, as for example just-different atoms, presented in de Salvo Braz et al. [2006]).”