They also illustrate the use of some recent advances in the theory of nonlinearempiricalprocesses.

In these cases, protected attributes can be "imputed" [

Our main contribution is an optimization framework to learn fair classifiers in this adversarial setting that comes with provable guarantees on accuracy and fairness.

Typicalness-A ware Learning (T AL) to address this issue and improve failure detection performance.