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- W4294568653 abstract "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." @default.
- W4294568653 created "2022-09-04" @default.
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- W4294568653 date "2016-02-11" @default.
- W4294568653 modified "2023-10-18" @default.
- W4294568653 title "Second-Order Stochastic Optimization for Machine Learning in Linear Time" @default.
- W4294568653 doi "https://doi.org/10.48550/arxiv.1602.03943" @default.
- W4294568653 hasPublicationYear "2016" @default.
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