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Evidence-Specific Structures for Rich Tractable CRFs
Anton Chechetka · Carlos Guestrin

Wed Dec 08 12:00 AM -- 12:00 AM (PST) @ None #None

We present a simple and effective approach to learning tractable conditional random fields with structure that depends on the evidence. Our approach retains the advantages of tractable discriminative models, namely efficient exact inference and exact parameter learning. At the same time, our algorithm does not suffer a large expressive power penalty inherent to fixed tractable structures. On real-life relational datasets, our approach matches or exceeds state of the art accuracy of the dense models, and at the same time provides an order of magnitude speedup

Author Information

Anton Chechetka (Carnegie Mellon University)
Carlos Guestrin (University of Washington)

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