Hypertree Decompositions Revisited for PGMs
  We revisit the classical problem of exact inference on probabilistic
graphical models (PGMs). Our algorithm is based on recent \emph{worst-case
optimal database join} algorithms, which can be asymptotically faster than
traditional data processing methods. We present the first empirical evaluation
of these algorithms via JoinInfer -- a new exact inference engine. We
empirically explore the properties of the data for which our engine can be
expected to outperform traditional inference engines, refining current
theoretical notions. Further, JoinInfer outperforms existing state-of-the-art
inference engines (ACE, IJGP and libDAI) on some standard benchmark datasets by
up to a factor of 630x. Finally, we propose a promising data-driven heuristic
that extends JoinInfer to automatically tailor its parameters and/or switch to
the traditional inference algorithms.
