Learning max-margin tree predictors

Structured prediction is a powerful framework for coping with joint prediction of interacting outputs. A central difficulty in using this framework is that often the correct label dependence structure is unknown. At the same time, we would like to avoid an overly complex structure that will lead to intractable prediction. In this work we address the challenge of learning tree structured predictive models that achieve high accuracy while at the same time facilitate efficient (linear time) inference. We start by proving that this task is in general NP-hard, and then suggest an approximate alternative. Our CRANK approach relies on a novel Circuit-RANK regularizer that penalizes non-tree structures and can be optimized using a convex-concave procedure. We demonstrate the effectiveness of our approach on several domains and show that its accuracy matches that of fully connected models, while performing prediction substantially faster.