ICML 2016poster85 citations
A New PAC-Bayesian Perspective on Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
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 PAC-Bayesian generalization bound, and specialize it to linear classifiers. Then, we infer a learning algorithm and perform experiments on real data.
BibTeX
@InProceedings{pmlr-v48-germain16,
title = {A New PAC-Bayesian Perspective on Domain Adaptation},
author = {Germain, Pascal and Habrard, Amaury and Laviolette, François and Morvant, Emilie},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {859--868},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/germain16.pdf},
url = {https://proceedings.mlr.press/v48/germain16.html},
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 PAC-Bayesian generalization bound, and specialize it to linear classifiers. Then, we infer a learning algorithm and perform experiments on real data.}
}