  For large nonlinear least squares loss functions in machine learning we
exploit the property that the number of model parameters typically exceeds the
data in one batch. This implies a low-rank structure in the Hessian of the
loss, which enables effective means to compute search directions. Using this
property, we develop two algorithms that estimate Jacobian matrices and perform
well when compared to state-of-the-art methods.
