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