We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice.
| Author(s): | Huang, J. and Smola, A. and Gretton, A. and Borgwardt, KM. and Schölkopf, B. |
| Book Title: | Advances in Neural Information Processing Systems 19 |
| Journal: | Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference |
| Pages: | 601-608 |
| Year: | 2007 |
| Month: | September |
| Day: | 0 |
| Editors: | B Sch{\"o}lkopf and J Platt and T Hofmann |
| Publisher: | MIT Press |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Cambridge, MA, USA |
| Event Name: | 20th Annual Conference on Neural Information Processing Systems (NIPS 2006) |
| Event Place: | Vancouver, BC, Canada |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 0-262-19568-2 |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{4194,
title = {Correcting Sample Selection Bias by Unlabeled Data},
journal = {Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference},
booktitle = {Advances in Neural Information Processing Systems 19},
abstract = {We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and
testing sets in feature space. Experimental results demonstrate that our method works well in practice.},
pages = {601-608},
editors = {B Sch{\"o}lkopf and J Platt and T Hofmann},
publisher = {MIT Press},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Cambridge, MA, USA},
month = sep,
year = {2007},
slug = {4194},
author = {Huang, J. and Smola, A. and Gretton, A. and Borgwardt, KM. and Sch{\"o}lkopf, B.},
month_numeric = {9}
}