Generating and Sampling Orbits for Lifted Probabilistic Inference

Steven Holtzen, Todd Millstein, Guy Van den Broeck
Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, PMLR 115:985-994, 2020.

Abstract

A key goal in the design of probabilistic inference algorithms is identifying and exploit- ing properties of the distribution that make inference tractable. Lifted inference algorithms identify symmetry as a property that enables efficient inference and seek to scale with the degree of symmetry of a probability model. A limitation of existing exact lifted inference techniques is that they do not apply to non- relational representations like factor graphs. In this work we provide the first example of an exact lifted inference algorithm for arbitrary discrete factor graphs. In addition we describe a lifted Markov-Chain Monte-Carlo algorithm that provably mixes rapidly in the degree of symmetry of the distribution.

Cite this Paper

BibTeX
@InProceedings{pmlr-v115-holtzen20a, title = {Generating and Sampling Orbits for Lifted Probabilistic Inference}, author = {Holtzen, Steven and Millstein, Todd and {Van den Broeck}, Guy}, booktitle = {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference}, pages = {985--994}, year = {2020}, editor = {Adams, Ryan P. and Gogate, Vibhav}, volume = {115}, series = {Proceedings of Machine Learning Research}, month = {22--25 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v115/holtzen20a/holtzen20a.pdf}, url = {https://proceedings.mlr.press/v115/holtzen20a.html}, abstract = {A key goal in the design of probabilistic inference algorithms is identifying and exploit- ing properties of the distribution that make inference tractable. Lifted inference algorithms identify symmetry as a property that enables efficient inference and seek to scale with the degree of symmetry of a probability model. A limitation of existing exact lifted inference techniques is that they do not apply to non- relational representations like factor graphs. In this work we provide the first example of an exact lifted inference algorithm for arbitrary discrete factor graphs. In addition we describe a lifted Markov-Chain Monte-Carlo algorithm that provably mixes rapidly in the degree of symmetry of the distribution.} }
Endnote
%0 Conference Paper %T Generating and Sampling Orbits for Lifted Probabilistic Inference %A Steven Holtzen %A Todd Millstein %A Guy Van den Broeck %B Proceedings of The 35th Uncertainty in Artificial Intelligence Conference %C Proceedings of Machine Learning Research %D 2020 %E Ryan P. Adams %E Vibhav Gogate %F pmlr-v115-holtzen20a %I PMLR %P 985--994 %U https://proceedings.mlr.press/v115/holtzen20a.html %V 115 %X A key goal in the design of probabilistic inference algorithms is identifying and exploit- ing properties of the distribution that make inference tractable. Lifted inference algorithms identify symmetry as a property that enables efficient inference and seek to scale with the degree of symmetry of a probability model. A limitation of existing exact lifted inference techniques is that they do not apply to non- relational representations like factor graphs. In this work we provide the first example of an exact lifted inference algorithm for arbitrary discrete factor graphs. In addition we describe a lifted Markov-Chain Monte-Carlo algorithm that provably mixes rapidly in the degree of symmetry of the distribution.
APA
Holtzen, S., Millstein, T. & Van den Broeck, G.. (2020). Generating and Sampling Orbits for Lifted Probabilistic Inference. Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, in Proceedings of Machine Learning Research 115:985-994 Available from https://proceedings.mlr.press/v115/holtzen20a.html.

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