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

Authors

76

  1. EC
    Elliot Creager

    University of Toronto, Canada

    2 publications · 61 citations

    2

    h-index

  2. LL
    Lingjuan Lyu

    Sony Computer Science Laboratories, Japan

    2 publications · 60 citations

    2

    h-index

  3. RS
    Richard S. Zemel

    University of Toronto, Canada

    2 publications · 61 citations

    2

    h-index

  4. AB
    Ahmad Beirami

    Georgia Institute of Technology, United States

    2 publications · 52 citations

    2

    h-index

  5. XX
    Xinyi Xu

    Xidian University, China

    2 publications · 60 citations

    2

    h-index

1–5 of 76

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 209 citations

    7

    h-index

  2. IEEE Access

    1 publications · 17 citations

    1

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 23 citations

    1

    h-index

  4. IEEE Transactions on Services Computing

    1 publications · 138 citations

    1

    h-index

  5. GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.

    Conference · 1 publications · 12 citations

    1

    h-index

1–5