Partial Inference in Structured Prediction
Chuyang Ke Jean Honorio
In this paper, we examine the problem of partial inference in the context of
structured prediction. Using a generative model approach, we consider the task
of maximizing a score function with unary and pairwise potentials in the space
of labels on graphs. Employing a two-stage convex optimization algorithm for
label recovery, we analyze the conditions under which a majority of the labels
can be recovered. We introduce a novel perspective on the Karush-Kuhn-Tucker
(KKT) conditions and primal and dual construction, and provide statistical and
topological requirements for partial recovery with provable guarantees.