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Many machine learning models depend on solving a large scale optimization problem.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
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12 fast training of support vector machines using sequential minimal optimization
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Robust stochastic approximation approach to stochastic programming
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On the use of stochastic hessian information in optimization methods for machine learning
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Convergence rates of sub-sampled newton methods
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Newton sketch: A linear-time optimization algorithm with linear-quadratic convergence
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Spectral graph theory
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Sub-sampled newton methods ii: Local convergence rates
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Sub-sampled newton methods i: globally convergent algorithms
Farbod Roosta-Khorasani and Michael W Mahoney
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