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  Fairness Constraints: A Flexible Approach for Fair Classification

Zafar, M. B., Valera, I., Gomez-Rodriguez, M., & Krishna, P. (2019). Fairness Constraints: A Flexible Approach for Fair Classification. Journal of Machine Learning Research, 20: 75. Retrieved from https://jmlr.csail.mit.edu/papers/v20/18-262.html.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0006-D28F-C Version Permalink: https://hdl.handle.net/21.11116/0000-000C-DE13-5
Genre: Journal Article

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 Creators:
Zafar, M. B.1, Author
Valera, I.2, Author           
Gomez-Rodriguez, M.1, Author
Krishna, P.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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Free keywords: Abt. Schölkopf
 Abstract: -

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Language(s): eng - English
 Dates: 2019-032019
 Publication Status: Issued
 Pages: 42
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: ZafValGomGum19
URI: https://jmlr.csail.mit.edu/papers/v20/18-262.html
URI: https://dl.acm.org/doi/10.5555/3322706.3362016
 Degree: -

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Title: Journal of Machine Learning Research
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Brookline, MA : Microtome Publishing
Pages: - Volume / Issue: 20 Sequence Number: 75 Start / End Page: - Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020