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  Responsible Model Deployment via Model-agnostic Uncertainty Learning

Lahoti, P., Gummadi, K., & Weikum, G. (2022). Responsible Model Deployment via Model-agnostic Uncertainty Learning. Machine Learning, 112, 939-970. doi:10.1007/s10994-022-06248-y.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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 Creators:
Lahoti, Preethi1, Author           
Gummadi, Krishna2, Author           
Weikum, Gerhard1, 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: 2022
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Lahoti2022
DOI: 10.1007/s10994-022-06248-y
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Project name : imPACT
Grant ID : 610150
Funding program : Funding Programme 7 (FP7)
Funding organization : European Commission (EC)
Project name : FairSocialComputing
Grant ID : 789373
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Machine Learning
Source Genre: Journal
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Publ. Info: Dordrecht : Springer
Pages: 32 p. Volume / Issue: 112 Sequence Number: - Start / End Page: 939 - 970 Identifier: ISSN: 0885-6125
CoNE: https://pure.mpg.de/cone/journals/resource/08856125