Second-Order Stochastic Optimization for Machine Learning in Linear Time
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| Authors | Brian Bullins, Naman Agarwal, Elad Hazan |
| Journal/Conference Name | Journal of Machine Learning Research |
| Paper Category | Artificial Intelligence |
| Paper 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. |
| Date of publication | 2016 |
| Code Programming Language | Python |
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