Uncertainty in Artificial Intelligence
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Bisimulation-based Approximate Lifted Inference
Prithviraj Sen, Amol Deshpande, Lise Getoor
Abstract:
There has been a great deal of recent interest in methods for performing lifted inference; however, most of this work assumes that the first-order model is given as input to the system. Here, we describe lifted inference algorithms that determine symmetries and automatically lift the probabilistic model to speedup inference. In particular, we describe approximate lifted inference techniques that allow the user to trade off inference accuracy for computational efficiency by using a handful of tunable parameters, while keeping the error bounded. Our algorithms are closely related to the graph-theoretic concept of bisimulation. We report experiments on both synthetic and real data to show that in the presence of symmetries, run-times for inference can be improved significantly, with approximate lifted inference providing orders of magnitude speedup over ground inference.
Keywords: null
Pages: 496-505
PS Link:
PDF Link: /papers/09/p496-sen.pdf
BibTex:
@INPROCEEDINGS{Sen09,
AUTHOR = "Prithviraj Sen and Amol Deshpande and Lise Getoor",
TITLE = "Bisimulation-based Approximate Lifted Inference",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "496--505"
}


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