AdaBoost and robust one-bit compressed sensing

Understand

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, prediction error bounds are derived.
• The developed theory is general and allows for heavy-tailed feature distributions, requiring only a weak moment assumption and an anti-concentration condition.