Lifted Inference via k-Locality

  title={Lifted Inference via k-Locality},
  author={Martin Mladenov and Kristian Kersting},
  booktitle={AAAI Workshop: Statistical Relational Artificial Intelligence},
Lifted inference approaches exploit symmetries of a graphical model. So far, only the automorphism group of the graphical model has been proposed to formalize the symmetries used. We show that this is only the GIcomplete tip of a hierarchy and that the amount of lifting depends on how local the inference algorithm is: if the LP relaxation introduces constraints involving features over at most k variables, then the amount of lifting decreases monotonically with k. This induces a hierarchy of… CONTINUE READING