Approximating Algorithmic Conditional Independence for Discrete Data

Marx, Alexander; Vreeken, Jilles

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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: https://hdl.handle.net/21.11116/0000-0003-0D4C-B Version Permalink: https://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: Accepted: 2019

Publication Status: Accepted / In Press

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Identifiers: BibTex Citekey: Marx_AAAISpringSymp2019

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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

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