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|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|
|Electronic Publication Date:||1-Nov-2017|
|Citation:||Agarwal, N, Bullins, B, Hazan, E. (2017). Second-order stochastic optimization for machine learning in linear time. Journal of Machine Learning Research, 18 (1 - 40|
|Pages:||1 - 40|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||Journal of Machine Learning Research|
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