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  Machine learning approaches to statistical dependences and causality

Janzing, D., Lauritzen, S., & Schölkopf, B. (2009). Machine learning approaches to statistical dependences and causality.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-8C1C-D Version Permalink: https://hdl.handle.net/21.11116/0000-0007-8DAB-A
Genre: Proceedings

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https://drops.dagstuhl.de/opus/volltexte/2010/2363/pdf/09401_abstracts_collection.2363.pdf (Publisher version)
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 Creators:
Janzing, D1, 2, Author           
Lauritzen, S, Author
Schölkopf, B1, 2, Author           
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497795              

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 Abstract: From 27.09.2009 to 02.10.2009, the Dagstuhl Seminar 09401 ``Machine learning approaches to statistical dependences and causality'' was held
in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.

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 Dates: 2009-09
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: -
 Degree: -

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Title: Dagstuhl Seminar: Machine learning approaches to statistical dependences and causality
Place of Event: Schloss Dagstuhl, Germany
Start-/End Date: 2009-09-27 - 2009-10-02

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Title: Dagstuhl Reports
Source Genre: Journal
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Publ. Info: Wadern : Schloss Dagstuhl, Leibniz-Zentrum für Informatik
Pages: 15 Volume / Issue: 09401 Sequence Number: - Start / End Page: - Identifier: ISSN: 2192-5283
CoNE: https://pure.mpg.de/cone/journals/resource/21925283