PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers

24 Mar 2015Pascal GermainAmaury HabrardFrançois LavioletteEmilie Morvant

In this paper, we provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a different target distribution. On the one hand, we propose an improvement of the previous approach proposed by Germain et al. (2013), that relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter PAC-Bayesian domain adaptation bound for the stochastic Gibbs classifier... (read more)

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