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A New PAC-Bayesian Perspective on Domain Adaptation
lib:8a61cc46d2dc81e8 (v1.0.0)
Authors: Pascal Germain,Amaury Habrard,François Laviolette,Emilie Morvant
ArXiv: 1506.04573
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1506.04573v4
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 PAC-Bayesian generalization bound, and specialize it to linear classifiers. Then, we infer a learning algorithmand perform experiments on real data.
ArXiv: 1506.04573
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1506.04573v4
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 PAC-Bayesian generalization bound, and specialize it to linear classifiers. Then, we infer a learning algorithmand perform experiments on real data.
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