Bisimulation-based Approximate Lifted Inference

TL;DR

This paper introduces approximate lifted inference algorithms that automatically identify symmetries in probabilistic models using bisimulation, enabling faster inference with controllable accuracy trade-offs.

Contribution

It presents novel algorithms for automatic symmetry detection and approximate lifted inference, improving efficiency without sacrificing bounded accuracy.

Findings

Significant speedups in inference times on synthetic and real data

Approximate lifted inference outperforms ground inference by orders of magnitude

Algorithms effectively balance accuracy and computational efficiency

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.