A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning
Abstract
We consider the situation in semi-supervised learning, where the "label sampling" mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method of moments for estimating this stochastic dependence using the unlabeled data. This is potentially useful for two distinct purposes: a. As an input to a super- vised learning procedure which can be used to "de-bias" its results using labeled data only and b. As a potentially interesting learning task in it- self. We present several examples to illustrate the practical usefulness of our method.
Cite
Text
Rosset et al. "A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning." Neural Information Processing Systems, 2004.Markdown
[Rosset et al. "A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/rosset2004neurips-method/)BibTeX
@inproceedings{rosset2004neurips-method,
title = {{A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning}},
author = {Rosset, Saharon and Zhu, Ji and Zou, Hui and Hastie, Trevor J.},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1161-1168},
url = {https://mlanthology.org/neurips/2004/rosset2004neurips-method/}
}