Explanation Robustness is the property of a machine learning explanation that characterizes its stability and consistency under minor perturbations or variations in the input data, model parameters, or underlying assumptions, thereby distinguishing it from explanations that are sensitive or fragile to such changes.
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1.6K
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143
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74
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2017–2026
39
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Publications
44
Beyond generalization: a theory of robustness in machine learning
Timo Freiesleben, Thomas Grote · Synthese · 2023 · 90 citations · Full text
Robustness in machine learning explanations
Leif Hancox-Li · 2020 · 79 citations
1–5 of 44
Authors
143
- PK
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- CA
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1–5 of 143
Institutions
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Technische Universität Berlin
Berlin, Germany
5 h-index
15 publications
699 citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
11 publications
318 citations
Centrum Wiskunde & Informatica
Amsterdam, The Netherlands
2 h-index
3 publications
105 citations
Auburn University
Auburn, United States
2 h-index
4 publications
118 citations
Korea University
Seoul, South Korea
2 h-index
2 publications
64 citations
University of Illinois Chicago
Chicago, United States
2 h-index
2 publications
59 citations
Google (United States)
Mountain View, United States
2 h-index
6 publications
118 citations
Aalborg University
Aalborg, Denmark
1 h-index
4 publications
152 citations
1–8 of 74
Venues
- arXiv (Cornell University)
Repository · 13 publications · 641 citations
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 2 publications · 38 citations
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- Pattern Recognition
2 publications · 82 citations
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- MIS Quarterly
1 publications · 30 citations
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- Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
Conference · 1 publications · 14 citations
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