Robust Fairness
Robust Fairness is a fairness framework in machine learning that ensures equitable model performance across diverse and potentially adversarially perturbed subgroups by maintaining fairness guarantees under distributional shifts and data uncertainty.
22
Publications
899
Citations
76
Authors
36
Institutions
Publications per year
2017–2026
19
Parents
22
1–5 of 22
76
2
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 76
36
California Southern University
Chandler, United States
2 h-index
4 publications
141 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
4 publications
134 citations

University of Toronto
Toronto, Canada
2 h-index
2 publications
61 citations
State Key Laboratory of Cryptology
Beijing, China
2 h-index
2 publications
206 citations
Apple (Germany)
Munich, Germany
2 h-index
2 publications
61 citations
National University of Singapore
Singapore, Singapore
2 h-index
5 publications
195 citations
Vector Institute
Toronto, Canada
2 h-index
2 publications
61 citations
University of Southern California
Los Angeles, United States
2 h-index
4 publications
141 citations
1–8 of 36
Repository · 7 publications · 209 citations
7
h-index
1 publications · 17 citations
1
h-index
Conference · 1 publications · 23 citations
1
h-index
1 publications · 138 citations
1
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Conference · 1 publications · 12 citations
1
h-index
1–5