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Quasi-Newton methods are widely used in practise for convex loss minimization problems.
A stochastic approximation method
H. Robbins and S. Monro · 1951
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Quasi-newton methods, motivation and theory
J. E. Dennis, Jr and J. J. Moré · 1977
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On the limited memory bfgs method for large scale optimization
D. C. Liu and J. Nocedal · 1989
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Numerical optimization
J. Nocedal and S. Wright · 1999
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A parallel mixture of svms for very large scale problems
R. Collobert, S. Bengio, and Y. Bengio · 2002
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A stochastic quasi-newton method for online convex optimization
N. Schraudolph, J. Yu, and S. Günter · 2007
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Large-scale machine learning with stochastic gradient descent
L. Bottou · 2010
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Non-asymptotic analysis of stochastic approximation algorithms for machine learning
F. Bach, E. Moulines, et al · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
J. Duchi, E. Hazan, and Y. Singer · 2011
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Making gradient descent optimal for strongly convex stochastic optimization
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Pegasos: Primal estimated sub-gradient solver for svm
S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter · 2011
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A stochastic gradient method with an exponential convergence rate for finite training sets
N. L. Roux, M. Schmidt, and F. R. Bach · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
R. Johnson and T. Zhang · 2013
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Semi-stochastic gradient descent methods
J. Konečnỳ and P. Richtárik · 2013
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A stochastic quasi-newton method for large-scale optimization
R. H. Byrd, S. Hansen, J. Nocedal, and Y. Singer · 2014
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Saga: A fast incremental gradient method with support for non-strongly convex composite objectives
A. Defazio, F. Bach, and S. Lacoste-Julien · 2014
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S. Lacoste-Julien, M. Schmidt, and F. Bach · 2012
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A. Mokhtari and A. Ribeiro · 2014
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