We tackle the PAC-Bayesian Domain Adaptation (DA) problem. This arrives when one desires to learn, from a source distribution, a good weighted majority vote (over a set of classifiers) on a different target distribution. In this context, the disagreement between classifiers is known crucial to control. In non-DA supervised setting, a theoretical bound - the C-bound - involves this disagreement and leads to a majority vote learning algorithm: MinCq. In this work, we extend MinCq to DA by taking advantage of an elegant divergence between distribution called the Perturbed Varation (PV). Firstly, justified by a new formulation of the C-bound, we provide to MinCq a target sample labeled thanks to a PV-based self-labeling focused on regions where the source and target marginal distributions are closer. Secondly, we propose an original process for tuning the hyperparameters. Our framework shows very promising results on a toy problem.

通过扩展MinCq算法，利用扰动方差，提出了一种针对PAC-Bayesian Domain Adaptation问题的解决方案，包括利用PV自标记目标样本并调整超参数，通过在目标和源概率分布相似的区域进行自标记并控制不同分类器之间的选票不一致性。在toy问题上获得了很好的结果。

通过扰动化基于变异的标签传递进行多数投票域适应