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A New PAC-Bayesian Perspective on Domain Adaptation

2020-03-06 | |  67 |   51 |   0

Abstract

We study the issue of PAC-Bayesian domain adaptation: We want to learn, from a source domain, a majority vote model dedicated to a target one. Our theoretical contribution brings a new perspective 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. Our bound suggests that one has to focus on regions where the source data is informative. From this result, we derive a PACBayesian generalization bound, and specialize it to linear classifiers. Then, we infer a learning a gorithm and perform experiments on real data.

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