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 language||English (US)|
|Journal||Advances in Neural Information Processing Systems|
|State||Published - 2005|
|Event||18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada|
Duration: Dec 13 2004 → Dec 16 2004