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

Nonlinear Least Squares for Large-Scale Machine Learning using Stochastic Jacobian Estimates