Structured Learning with Approximate Inference
Alex Kulesza and Fernando Pereira, University of Pennsylvania [M43]

Approximate Inference Nearly optimal Learning

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Approximate Inference

· We show that inference approximations (like LPs or loopy BP) can · Dramatically increase learning bias · Badly mislead standard learning algorithms · For inference used in learning, compatibility is as important as quality of approximation