Estimating Labels from Label Proportions
Abstract
Consider the following problem: given sets of unlabeled observations, each set with known label proportions, predict the labels of another set of observations, possibly with known label proportions. This problem occurs in areas like e-commerce, politics, spam filtering and improper content detection. We present consistent estimators which can reconstruct the correct labels with high probability in a uniform convergence sense. Experiments show that our method works well in practice.
Cite
Text
Quadrianto et al. "Estimating Labels from Label Proportions." Journal of Machine Learning Research, 2009.Markdown
[Quadrianto et al. "Estimating Labels from Label Proportions." Journal of Machine Learning Research, 2009.](https://mlanthology.org/jmlr/2009/quadrianto2009jmlr-estimating/)BibTeX
@article{quadrianto2009jmlr-estimating,
title = {{Estimating Labels from Label Proportions}},
author = {Quadrianto, Novi and Smola, Alex J. and Caetano, Tibério S. and Le, Quoc V.},
journal = {Journal of Machine Learning Research},
year = {2009},
pages = {2349-2374},
volume = {10},
url = {https://mlanthology.org/jmlr/2009/quadrianto2009jmlr-estimating/}
}