fair-representation-learning

This paper proposes a joint-distribution approach to fair representation learning with continuous sensitive attributes, using HSIC as a joint discrepancy to avoid conditional density estimation. It proves statistical efficiency gains over conditional-route methods and introduces the FRHSIC algorithm with comparable fairness-accuracy tradeoffs and faster training.