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  On causal and anticausal learning

Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., & Mooij, J. (2012). On causal and anticausal learning. In J. Langford, & J. Pineau (Eds.), 29th International Conference on Machine Learning (ICML 2012) (pp. 1255-1262). Madison, WI, USA: International Machine Learning Society.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B6C6-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-1BB5-5
Genre: Conference Paper

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Locator:
https://icml.cc/2012/papers/625.pdf (Publisher version)
Description:
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 Creators:
Schölkopf, B1, Author              
Janzing, D1, Author              
Peters, J1, Author              
Sgouritsa, E1, Author              
Zhang, K1, Author              
Mooij, J, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: We consider the problem of function estimation in the case where an underlying causal model can be inferred. This has implications for popular scenarios such as covariate shift, concept drift, transfer learning and semi-supervised learning. We argue that causal knowledge may facilitate some approaches for a given problem, and rule out others. In particular, we formulate a hypothesis for when semi-supervised learning can help, and corroborate it with empirical results.

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 Dates: 2012-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: ScholkopfJPSZM2012
 Degree: -

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Title: 29th International Conference on Machine Learning (ICML 2012)
Place of Event: Edinburgh, UK
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Title: 29th International Conference on Machine Learning (ICML 2012)
Source Genre: Proceedings
 Creator(s):
Langford, J, Editor
Pineau, J, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1255 - 1262 Identifier: ISBN: 978-1-4503-1285-1