  First-order stochastic methods are the state-of-the-art in large-scale
machine learning optimization owing to efficient per-iteration complexity.
Second-order methods, while able to provide faster convergence, have been much
less explored due to the high cost of computing the second-order information.
In this paper we develop second-order stochastic methods for optimization
problems in machine learning that match the per-iteration cost of gradient
based methods, and in certain settings improve upon the overall running time
over popular first-order methods. Furthermore, our algorithm has the desirable
property of being implementable in time linear in the sparsity of the input
data.
