A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning

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.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie
• Keywords: Interesting Learning Task, NIPS 2004, NIPS 2007, Semi-supervised Learning, Supervised Learning Procedure