AdaBoost and robust one-bit compressed sensing

This paper studies binary classification in robust one-bit compressed sensing with adversarial errors. It is assumed that the model is overparameterized and that the parameter of interest is effectively sparse. AdaBoost is considered, and, through its relation to the max-$\ell_1$-margin-classifier, risk bounds are derived. In particular, this provides an explanation why interpolating adversarial noise can be harmless for classification problems. Simulations illustrate the presented theory.