Generating and Sampling Orbits for Lifted Probabilistic Inference
Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, PMLR 115:985-994, 2020.
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.