Revisiting Agnostic PAC Learning

PAC learning, dating back to Valiant'84 and Vapnik and Chervonenkis'64,'74, is a classic model for studying supervised learning. In the agnostic setting, we have access to a hypothesis set $\mathbf{H}$ and a training set of labeled samples drawn i,i $\mathbf{d}$. from an unknown data distribution D. The goal is to produce a classifier that is competitive with the hypothesis in $\mathbf{H}$ having the least probability of mispredicting the label of a new sample from D. Empirical Risk Minimization (ERM) is a natural learning algorithm, where one simply outputs the hypothesis from $\mathbf{H}$ making the fewest mistakes on the training data. This simple algorithm is known to have an optimal error in terms of the VC-dimension of $\mathbf{H}$ and the number of samples. In this work, we revisit agnostic PAC learning and first show that ERM and any other proper learning algorithm is in fact sub-optimal if we treat the performance of the best hypothesis in $\mathbf{H}$, as a parameter. We then complement this lower bound with the first learning algorithm achieving an optimal error. Our algorithm introduces several new ideas that we hope may find further applications in learning theory.