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PAC-Bayes and Domain Adaptation

Pascal Germain 1, 2 Amaury Habrard 3 François Laviolette 2 Emilie Morvant 3
1 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : 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 Germain et al. (2013), 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 the spirit of common domain adaptation works, we derive a second bound (introduced in Germain et al., 2016) 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.
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Soumis le : mercredi 6 novembre 2019 - 14:08:58
Dernière modification le : mardi 10 novembre 2020 - 11:14:06
Archivage à long terme le : : samedi 8 février 2020 - 02:23:35

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Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. PAC-Bayes and Domain Adaptation. Neurocomputing, Elsevier, 2020, 379, pp.379-397. ⟨10.1016/j.neucom.2019.10.105⟩. ⟨hal-01563152v3⟩

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