Poor starting points in machine learning

Poor (even random) starting points for learning/training/optimization are common in machine learning. In many settings, the method of Robbins and Monro (online stochastic gradient descent) is known to be optimal for good starting points, but may not be optimal for poor starting points — indeed, for poor starting points Nesterov acceleration can help during the initial iterations, even though Nesterov methods not designed for stochastic approximation could hurt during later iterations. A good option is to roll off Nesterov acceleration for later iterations. The common practice of training with nontrivial minibatches enhances the advantage of Nesterov acceleration.