A method for inferring label sampling mechanisms in semi-supervised learning

Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie

Research output: Contribution to journalArticle

12 Scopus citations

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 supervised learning procedure which can be used to "de-bias" its results using labeled data only and b. As a potentially interesting learning task in itself. We present several examples to illustrate the practical usefulness of our method.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
Volume17
StatePublished - 2005
Event18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada
Duration: Dec 13 2004Dec 16 2004

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