Lecture 3 : Agnostic Learning and Bias-Complexity tradeoffs

The Agnostic PAC-learning framework is a way to relax the assumption of inherent label noise in the PAC learning model, and instead has an arbitrary distribution D over X × Y where X is the example space and Y is the label space.