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The paper studies the solution of stochastic optimization problems in which approximations to the gradient and Hessian are obtained through subsampling.
Inexact-Newton methods
R. S. Dembo, S. C. Eisenstat, and T. Steihaug · 1982
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Matrix Computations
G. H. Golub and C. F. Van Loan · 1989
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Nonlinear Programming
D. P. Bertsekas · 1995
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Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
Jock A Blackard and Denis J Dean · 1999
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Numerical Optimization
Jorge Nocedal and Stephen Wright · 1999
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Result analysis of the NIPS 2003 feature selection challenge
Isabelle Guyon, Steve Gunn, Asa Ben-Hur, and Gideon Dror · 2004
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L. Bottou and Y. Le Cun · 2005
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Leon Bottou and Olivier Bousquet · 2008
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A marketing dataset
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and Christopher JC Burges · 2010
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Deep learning via Hessian-free optimization
J. Martens · 2010
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Computational methods for sparse solution of linear inverse problems
Joel A Tropp and Stephen J Wright · 2010
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On the use of stochastic Hessian information in optimization methods for machine learning
Richard H Byrd, Gillian M Chin, Will Neveitt, and Jorge Nocedal · 2011
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Sample size selection in optimization methods for machine learning
Richard H Byrd, Gillian M Chin, Jorge Nocedal, and Yuchen Wu · 2012
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Hybrid deterministic-stochastic methods for data fitting
Michael P Friedlander and Mark Schmidt · 2012
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Convergence rates of sub-sampled newton methods
Murat A Erdogdu and Andrea Montanari · 2015
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Handbook of simulation optimization
Michael Fu et al · 2015
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On sampling rates in stochastic recursions
R. Pasupathy, P. W. Glynn, S. Ghosh, and F. Hashemi · 2015
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Newton sketch: A linear-time optimization algorithm with linear-quadratic convergence
Mert Pilanci and Martin J Wainwright · 2015
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Second order stochastic optimization in linear time
Naman Agarwal, Brian Bullins, and Elad Hazan · 2016
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Sub-sampled Newton methods I: Globally convergent algorithms
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UCI machine learning repository
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Indraneel Mukherjee, Kevin Canini, Rafael Frongillo, and Yoram Singer · 2013
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Simulation optimization: a review of algorithms and applications
Satyajith Amaran, Nikolaos V Sahinidis, Bikram Sharda, and Scott J Bury · 2014
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Roosta-Khorasani and Michael W Mahoney · 2016
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Sub-sampled Newton methods II: Local convergence rates
Farbod Roosta-Khorasani and Michael W Mahoney · 2016
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Sub-sampled Newton methods with non-uniform sampling
Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, and Michael W Mahoney · 2016
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