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Sketching, a dimensionality reduction technique, has received much attention in the statistics community.
A stochastic approximation method
H. Robbins and S. Monro · 1951
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Linear and Nonlinear Programming
David G. Luenberger · 1984
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Matrix Computations
Gene H. Golub and Charles F. Van Loan · 1989
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Learning algorithms for classification: A comparison on handwritten digit recognition
Yann LeCun, LD Jackel, Léon Bottou, Corinna Cortes, John S Denker, Harris Drucker, Isabelle Guyon, UA Muller, E Sackinger, Patrice Simard, et al · 1995
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Design and analysis of the causation and prediction challenge
Isabelle Guyon, Constantin F Aliferis, Gregory F Cooper, André Elisseeff, Jean-Philippe Pellet, Peter Spirtes, and Alexander R Statnikov · 2008
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LIBSVM: A library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
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Faster least squares approximation
Petros Drineas, Michael W Mahoney, S Muthukrishnan, and Tamás Sarlós · 2011
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New and improved johnson–lindenstrauss embeddings via the restricted isometry property
Felix Krahmer and Rachel Ward · 2011
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Optimization for machine learning
Suvrit Sra, Sebastian Nowozin, and Stephen J Wright · 2012
Earlier work this paper cites.
Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
Cited alongside, same era.
Parallel boosting with momentum
Indraneel Mukherjee, Kevin Canini, Rafael Frongillo, and Yoram Singer · 2013
Cited alongside, same era.
Introductory lectures on convex optimization: A basic course
Yurii Nesterov · 2013
Cited alongside, same era.
Sketching as a tool for numerical linear algebra
David P Woodruff et al · 2014
Cited alongside, same era.
Convergence rates of sub-sampled newton methods
Murat A Erdogdu and Andrea Montanari · 2015
Cited alongside, same era.
Coordinate descent algorithms
Stephen J Wright · 2015
Cited alongside, same era.
Optimization methods for large-scale machine learning
Léon Bottou, Frank E Curtis, and Jorge Nocedal · 2017
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Newton sketch: A near linear-time optimization algorithm with linear-quadratic convergence
Mert Pilanci and Martin J Wainwright · 2017
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Sketching meets random projection in the dual: A provable recovery algorithm for big and high-dimensional data
Jialei Wang, Jason D Lee, Mehrdad Mahdavi, Mladen Kolar, Nathan Srebro, et al · 2017
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Second-order optimization for non-convex machine learning: An empirical study
Peng Xu, Farbod Roosta-Khorasan, and Michael W Mahoney · 2017
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Newton-type methods for non-convex optimization under inexact hessian information
Peng Xu, Farbod Roosta-Khorasani, and Michael W Mahoney · 2017
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Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, and John Langford · 2016
Cited alongside, same era.
Sub-sampled newton methods with non-uniform sampling
Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, and Michael W Mahoney · 2016
Cited alongside, same era.
Second-order stochastic optimization for machine learning in linear time
Naman Agarwal, Brian Bullins, and Elad Hazan · 2017
Cited alongside, same era.
Sub-sampled newton methods
Farbod Roosta-Khorasani and Michael W Mahoney
Cited in the paper.
Exact and inexact subsampled newton methods for optimization
Raghu Bollapragada, Richard H Byrd, and Jorge Nocedal · 2018
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Sketched ridge regression: Optimization perspective, statistical perspective, and model averaging
Shusen Wang, Alex Gittens, and Michael W Mahoney · 2018
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Inexact non-convex newton-type methods
Zhewei Yao, Peng Xu, Farbod Roosta-Khorasani, and Michael W Mahoney · 2018
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