Partial Inference in Structured Prediction
This work addresses partial inference in structured prediction, which is an incremental improvement for researchers in machine learning and optimization.
The paper tackles the problem of partial inference in structured prediction by using a generative model and a two-stage convex optimization algorithm to maximize a score function with unary and pairwise potentials on graphs, analyzing conditions for recovering a majority of labels with provable guarantees.
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.