PAC-Bayes and Domain Adaptation

Pascal Germain, Amaury Habrard, Francois Laviolette, Emilie Morvant
2019 Neurocomputing  
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, but related, target distribution. Firstly, we propose an improvement of the previous approach we proposed in [1], which relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter domain adaptation bound for the target risk. While this bound stands in
more » ... spirit of common domain adaptation works, we derive a second bound (introduced in [2]) that brings a new perspective on domain adaptation by deriving an upper bound on the target risk where the distributions' divergence-expressed as a ratio-controls the trade-off between a source error measure and the target voters' disagreement. We discuss and compare both results, from which we obtain PAC-Bayesian generalization bounds. Furthermore, from the PAC-Bayesian specialization to linear classifiers, we infer two learning algorithms, and we evaluate them on real data.
doi:10.1016/j.neucom.2019.10.105 fatcat:kxc2yfvawvhnnnef5bdwxxlif4