We discuss stability for a class of learning algorithms with respect to noisy labels. The algorithms we consider are for regression, and they involve the minimization of regularized risk functionals, such as L(f) := 1 N PN i=1(f(xi)− yi) +λ‖f‖H. We shall call the algorithm ‘stable’ if, when yi is a noisy version of f(xi) for some functionf ∈ H, the output of the algorithm converges to f as the regularization term and noise simultaneously vanish. We consider two flavors of this problem, one… CONTINUE READING