  We present and analyze several strategies for improving the performance of
stochastic variance-reduced gradient (SVRG) methods. We first show that the
convergence rate of these methods can be preserved under a decreasing sequence
of errors in the control variate, and use this to derive variants of SVRG that
use growing-batch strategies to reduce the number of gradient calculations
required in the early iterations. We further (i) show how to exploit support
vectors to reduce the number of gradient computations in the later iterations,
(ii) prove that the commonly-used regularized SVRG iteration is justified and
improves the convergence rate, (iii) consider alternate mini-batch selection
strategies, and (iv) consider the generalization error of the method.
