Probabilistic structured predictors

Authors:
Shankar Vembu;Thomas Gärtner;Mario Boley
Affiliations:
Fraunhofer IAIS, Sankt Augustin, Germany;Fraunhofer IAIS, Sankt Augustin, Germany;Fraunhofer IAIS, Sankt Augustin, Germany
Venue:
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Year:
2009

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Abstract

We consider MAP estimators for structured prediction with exponential family models. In particular, we concentrate on the case that efficient algorithms for uniform sampling from the output space exist. We show that under this assumption (i) exact computation of the partition function remains a hard problem, and (ii) the partition function and the gradient of the log partition function can be approximated efficiently. Our main result is an approximation scheme for the partition function based on Markov Chain Monte Carlo theory. We also show that the efficient uniform sampling assumption holds in several application settings that are of importance in machine learning.