Abstract 

Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set. They have recently received increasing attention for developing optimization algorithms with fast convergence. However, the studies of EBC in statistical learning are hitherto still limited. The main contributions of this paper are two-fold. First, we develop fast and intermediate rates of empirical risk minimization (ERM) under EBC for risk minimization with Lipschitz continuous, and smooth convex random functions. Second, we establish fast and intermediate rates of an efficient stochastic approximation (SA) algorithm for risk minimization with Lipschitz continuous random functions, which requires only one pass of n samples and adapts to EBC. For both approaches, the convergence rates span a full spectrum between and e depending on the power constant in EBC, and could be even faster than in special cases for ERM. Moreover, these convergence rates are automatically adaptive without using any knowledge of EBC.