Abstract
The talk will survey recent progress on the computational complexity of binary classification in the presence of benign label noise. In particular, we will will give an overview of the key ideas behind known algorithms and computational hardness results for learning halfspaces (and other concept classes) in both the distribution free and the distribution specific PAC model.