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

Asish Ghoshal; Jean Honorio

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arXiv · 1805.08196

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

Asish Ghoshal·Jean Honorio

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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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Asish Ghoshal, Jean Honorio. 2018-05-21. Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time. https://arxiv.org/abs/1805.08196

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