On Learning Fairness and Accuracy on Multiple Subgroups

Neural Information Processing Systems 

In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets.

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