Information Leakage
Information Leakage is an error in machine learning model evaluation wherein data from outside the training dataset is inadvertently used during model development, leading to overestimated performance by violating the assumption of independent and identically distributed test conditions.
1.4K
Publications
66.7K
Citations
3.9K
Authors
1.1K
Institutions
Publications per year
2017–2026
716
1.4K
Certifying and Removing Disparate Impact
Michael Feldman, Sorelle A. Friedler, John Moeller et al. · 2015 · 1.8K citations
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3.9K
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Laboratoire d'Informatique de l'École Polytechnique, France
10 publications · 611 citations
10
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9
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1–5 of 3.9K
1.1K

Xidian University
Xi'an, China
17 h-index
67 publications
2.8K citations
Princeton University
Princeton, United States
17 h-index
47 publications
4.8K citations

Shanghai Jiao Tong University
Shanghai, China
17 h-index
66 publications
2.9K citations
University of Illinois Urbana-Champaign
Urbana, United States
16 h-index
36 publications
1.2K citations
National University of Singapore
Singapore, Singapore
16 h-index
39 publications
3.7K citations
Georgia Institute of Technology
Atlanta, United States
16 h-index
40 publications
2.3K citations
Tsinghua University
Beijing, China
16 h-index
43 publications
2.1K citations

Centre National de la Recherche Scientifique
Paris, France
15 h-index
44 publications
2.2K citations
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41 publications · 4.8K citations
31
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42 publications · 1.3K citations
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Repository · 52 publications · 1.7K citations
21
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19 publications · 699 citations
15
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Repository · 23 publications · 474 citations
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