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stat.ML2026
Counterfactually Fair Regression via Optimal Transport
M. Generali Lince, S. Gaucher, J-J. Vie +1
We consider the problem of learning a counterfactually fair regressor. We adopt a causal uncertainty view in which counterfactual fairness is defined with resampled noise. We focus…
stat.ML2026
Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings
M. Generali Lince, V. Divol, R. Flamary +2
Fairness-accuracy trade-offs are a central concern in the deployment of fairness-aware machine learning methods. When sensitive attributes are unavailable at inference time-the so…