Generalized Collective Inference with Symmetric Clique Potentials
  Collective graphical models exploit inter-instance associative dependence to
output more accurate labelings. However existing models support very limited
kind of associativity which restricts accuracy gains. This paper makes two
major contributions. First, we propose a general collective inference framework
that biases data instances to agree on a set of {\em properties} of their
labelings. Agreement is encouraged through symmetric clique potentials. We show
that rich properties leads to bigger gains, and present a systematic inference
procedure for a large class of such properties. The procedure performs message
passing on the cluster graph, where property-aware messages are computed with
cluster specific algorithms. This provides an inference-only solution for
domain adaptation. Our experiments on bibliographic information extraction
illustrate significant test error reduction over unseen domains. Our second
major contribution consists of algorithms for computing outgoing messages from
clique clusters with symmetric clique potentials. Our algorithms are exact for
arbitrary symmetric potentials on binary labels and for max-like and
majority-like potentials on multiple labels. For majority potentials, we also
provide an efficient Lagrangian Relaxation based algorithm that compares
favorably with the exact algorithm. We present a 13/15-approximation algorithm
for the NP-hard Potts potential, with runtime sub-quadratic in the clique size.
In contrast, the best known previous guarantee for graphs with Potts potentials
is only 1/2. We empirically show that our method for Potts potentials is an
order of magnitude faster than the best alternatives, and our Lagrangian
Relaxation based algorithm for majority potentials beats the best applicable
heuristic -- ICM.
