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

Saharon Rosset; Ji Zhu; Hui Zou; Trevor Hastie

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

Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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
Title of host publication	Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
Publisher	Neural information processing systems foundation
ISBN (Print)	0262195348, 9780262195348
State	Published - 2005
Externally published	Yes
Event	18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada Duration: 13 Dec 2004 → 16 Dec 2004

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Conference

Conference	18th Annual Conference on Neural Information Processing Systems, NIPS 2004
Country/Territory	Canada
City	Vancouver, BC
Period	13/12/04 → 16/12/04

Cite this

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title = "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 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.",

author = "Saharon Rosset and Ji Zhu and Hui Zou and Trevor Hastie",

year = "2005",

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series = "Advances in Neural Information Processing Systems",

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note = "18th Annual Conference on Neural Information Processing Systems, NIPS 2004 ; Conference date: 13-12-2004 Through 16-12-2004",

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Rosset, S, Zhu, J, Zou, H & Hastie, T 2005, A method for inferring label sampling mechanisms in semi-supervised learning. in Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004. Advances in Neural Information Processing Systems, Neural information processing systems foundation, 18th Annual Conference on Neural Information Processing Systems, NIPS 2004, Vancouver, BC, Canada, 13/12/04.

A method for inferring label sampling mechanisms in semi-supervised learning. / Rosset, Saharon; Zhu, Ji; Zou, Hui et al.
Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004. Neural information processing systems foundation, 2005. (Advances in Neural Information Processing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhu, Ji

AU - Zou, Hui

AU - Hastie, Trevor

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N2 - 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.

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T3 - Advances in Neural Information Processing Systems

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ER -

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

Abstract

Publication series

Conference

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