Lifted First-Order Belief Propagation

By Parag Singla, Pedro M. Domingos. AAAI Conference on Artificial Intelligence, 2008

300 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d14460144b6d7a1b35e9087d8db20b939124b4c5

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Citations in the corpus

Counting Belief Propagation (2009, background): “Acknowledgements The authors would like to thank Pedro Domingos, Parag Singla, and the anonymous reviewers for their comments.”
Lifted Stochastic Planning, Belief Propagation and Marginal MAP (2018, background): “First, similar to (Kersting, Ah-madi, and Natarajan 2009) and unlike (Singla and Domingos 2008), our algorithm for constructing the lifted graph must ﬁrst process the ground network, i.e., its complexity is linear in the size of the original ground computation graph.”
Automorphism Groups of Graphical Models and Lifted Variational Inference (2012, background): “A more recent approach known as lifted inference [2, 12, 6, 7] has demonstrated the possibility to perform very efficient inference in highly-connected, but symmetric models such as those arising in the context of relational (or first-order) probabilistic models.”
Markov Chains on Orbits of Permutation Groups (2012, methodology): “Following Poole’s work, several algorithms for lifted probabilistic inference were developed such as lifted and counting belief propagation [41, 22], bi-simulation-based approximate inference [40], general purpose MCMC algorithm for relational models [29] and, more recently, ﬁrst-order knowledge…”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, methodology): “Various inference techniques for discrete MRFs have been extended to a lifted approach, including belief propagation (Jaimovich et al., 2007; Singla and Domingos, 2008; Kersting et al., 2009) and Gibbs sampling (Venugopal and Gogate, 2012).”
Gradient-based boosting for statistical relational learning: The relational dependency network case (2011, methodology): “Though there are several lifted inference techniques proposed [45, 24, 39, 10, 31] for SRL models, the research in this area is at a nascent stage and boosting would provide the perfect opportunity to learn the structure and parameters simultaneously.”
Reasoning about Object Affordances in a Knowledge Base Representation (2014, methodology): “We use lifted belief propagation for inference [31].”
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation (2017, background): “The opportunity is that these large models have a great deal of symmetry, structure that can be exploited by “lifted” inference algorithms (cf. (Braz, Amir, & Roth, 2005; Singla & Domingos, 2008)).”
Efficient Graph Kernels by Randomization (2012, background): “Second, propagation kernels are deeply connected to several recent lifted message-passing approaches [1, 11, 15, 20] to probabilistic inference.”
Learning Markov Logic Networks via Functional Gradient Boosting (2011, background): “Since MLNs provide clear semantics and convergent inference approaches [28], they are among the most popular SRL methods.”