Web: http://arxiv.org/abs/2206.06531

June 20, 2022, 1:12 a.m. | Dounia Lakhmiri, Dominique Orban, Andrea Lodi

stat.ML updates on arXiv.org arxiv.org

We consider the problem of training a deep neural network with nonsmooth
regularization to retrieve a sparse and efficient sub-structure. Our
regularizer is only assumed to be lower semi-continuous and prox-bounded. We
combine an adaptive quadratic regularization approach with proximal stochastic
gradient principles to derive a new solver, called SR2, whose convergence and
worst-case complexity are established without knowledge or approximation of the
gradient's Lipschitz constant. We formulate a stopping criteria that ensures an
appropriate first-order stationarity measure converges to …

arxiv ml optimization stochastic

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