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  Approximating Algorithmic Conditional Independence for Discrete Data

Marx, A., & Vreeken, J. (in press). Approximating Algorithmic Conditional Independence for Discrete Data. In Proceedings of the First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-0D4C-B Version Permalink: http://hdl.handle.net/21.11116/0000-0003-0D4D-A
Genre: Conference Paper

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 Creators:
Marx, Alexander1, Author              
Vreeken, Jilles2, Author              
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 2019
 Publication Status: Accepted / In Press
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Marx_AAAISpringSymp2019
 Degree: -

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Title: First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI
Place of Event: Stanford, CA, USA
Start-/End Date: 2019-05-25 - 2019-05-27

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Title: Proceedings of the First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -