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

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Asish Ghoshal, Jean Honorio ;
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:1754-1762, 2018.

Abstract

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.

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