A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers


Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):738-746, 2013.


We provide a first PAC-Bayesian analysis for domain adaptation (DA) which arises when the learning and test distributions differ. It relies on a novel distribution pseudodistance based on a disagreement averaging. Using this measure, we derive a PAC-Bayesian DA bound for the stochastic Gibbs classifier. This bound has the advantage of being directly optimizable for any hypothesis space. We specialize it to linear classifiers, and design a learning algorithm which shows interesting results on a synthetic problem and on a popular sentiment annotation task. This opens the door to tackling DA tasks by making use of all the PAC-Bayesian tools.

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