Uncertainty in Artificial Intelligence
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Learning Max-Margin Tree Predictors
Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson
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. Briefly, our CRANK approach relies on a novel Circuit-RANK regularizer that penalizes non-tree structures and that can be optimized using a CCCP procedure. We demonstrate the effectiveness of our approach on several domains and show that, despite the relative simplicity of the structure, prediction accuracy is competitive with a fully connected model that is computationally costly at prediction time.
Pages: 411-420
PS Link:
PDF Link: /papers/13/p411-meshi.pdf
AUTHOR = "Ofer Meshi and Elad Eban and Gal Elidan and Amir Globerson",
TITLE = "Learning Max-Margin Tree Predictors",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "411--420"

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