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MAP perturbation models have emerged as a powerful framework for inference instructured prediction. Such models provide a way to efficiently sample from theGibbs distribution and facilitate predictions that are robust to random noise.In this paper, we propose a provably polynomial time randomized algorithm forlearning the parameters of perturbed MAP predictors. Our approach is based onminimizing a novel Rademacher-based generalization bound on the expected lossof a perturbed MAP predictor, which can be computed in polynomial time. Weobtain conditions under which our randomized learning algorithm can guaranteegeneralization to unseen examples.

Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time