A post-processing algorithm achieves distribution-free finite-sample group fairness guarantees with controlled excess risk for both group-aware and group-blind settings, shown minimax-optimal up to logs via lower bound.
Then |λ∗ α| = 0 is the smallest non-negative real number λ+ such that sEϕG(X, A)1 2ηG(X, A) − 1 > sλ+ϕG(X, A) ≤ α
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Finite-Sample and Distribution-Free Fair Classification: Optimal Trade-off Between Excess Risk and Fairness, and the Cost of Group-Blindness
A post-processing algorithm achieves distribution-free finite-sample group fairness guarantees with controlled excess risk for both group-aware and group-blind settings, shown minimax-optimal up to logs via lower bound.