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Scalable Fair Learning via Cramér-von Mises Regularization
A Cramér-von Mises fairness regularizer with O(B log B) complexity penalizes prediction-sensitive attribute dependence during training, achieving competitive fairness-utility trade-offs with lower overhead.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 0/5