  In this paper, we propose a novel approach to automatically determine the
batch size in stochastic gradient descent methods. The choice of the batch size
induces a trade-off between the accuracy of the gradient estimate and the cost
in terms of samples of each update. We propose to determine the batch size by
optimizing the ratio between a lower bound to a linear or quadratic Taylor
approximation of the expected improvement and the number of samples used to
estimate the gradient. The performance of the proposed approach is empirically
compared with related methods on popular classification tasks.
  The work was presented at the NIPS workshop on Optimizing the Optimizers.
Barcelona, Spain, 2016.
