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 language | English (US) |
|---|---|
| Journal | Advances in Neural Information Processing Systems |
| Volume | 17 |
| State | Published - 2005 |
| Event | 18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada Duration: Dec 13 2004 → Dec 16 2004 |