Fetching the paper…
Reading the bibliography…
We propose a fast proximal Newton-type algorithm for minimizing regularized finite sums that returns an $\epsilon$-suboptimal point in $\tilde{\mathcal{O}}(d(n + \sqrt{\kappa d})\log(\frac{1}{\epsilon}))$ FLOPS, where $n$ is number of samples, $d$ is feature dimension, and $\kappa$ is the condition number.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
Earlier work this paper cites.
Introductory Lectures on Convex Optimization , volume 87
Yurii Nesterov · 2004
Earlier work this paper cites.
A modified finite newton method for fast solution of large scale linear svms
S Sathiya Keerthi and Dennis DeCoste · 2005
Earlier work this paper cites.
Pathwise coordinate optimization
J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani · 2007
Earlier work this paper cites.
A stochastic quasi-newton method for online convex optimization
Nicol N Schraudolph, Jin Yu, Simon Günter, et al · 2007
Earlier work this paper cites.
Liblinear: A library for large linear classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin · 2008
Earlier work this paper cites.
Trust region newton method for logistic regression
Chih-Jen Lin, Ruby C Weng, and S Sathiya Keerthi · 2008
Earlier work this paper cites.
A fast hybrid algorithm for large-scale l1-regularized logistic regression
Jianing Shi, Wotao Yin, Stanley Osher, and Paul Sajda · 2010
Earlier work this paper cites.
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
Earlier work this paper cites.
Sparse inverse covariance matrix estimation using quadratic approximation
C.J. Hsieh, M.A. Sustik, I.S. Dhillon, and P. Ravikumar · 2011
Earlier work this paper cites.
Fast approximation of matrix coherence and statistical leverage
Petros Drineas, Malik Magdon-Ismail, Michael W Mahoney, and David P Woodruff · 2012
Earlier work this paper cites.
A stochastic gradient method with an exponential convergence _rate for finite training sets
Nicolas L Roux, Mark Schmidt, and Francis R Bach · 2012
Cited alongside, same era.
An improved glmnet for ℓ 1 \ell_{1} -regularized logistic regression
G.X. Yuan, C.H. Ho, and C.J. Lin · 2012
Cited alongside, same era.
An inexact successive quadratic approximation method for convex l-1 regularized optimization
Richard H Byrd, Jorge Nocedal, and Figen Oztoprak · 2013
Cited alongside, same era.
Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
Cited alongside, same era.
Stochastic dual coordinate ascent methods for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2013
Cited alongside, same era.
Convergence rates of sub-sampled newton methods
Murat A Erdogdu and Andrea Montanari · 2015
Later among the works it cites.
A universal catalyst for first-order optimization
Hongzhou Lin, Julien Mairal, and Zaid Harchaoui · 2015
Later among the works it cites.
Newton sketch: A linear-time optimization algorithm with linear-quadratic convergence
Mert Pilanci and Martin J Wainwright · 2015
Later among the works it cites.
Composite self-concordant minimization
Quoc Tran-Dinh, Anastasios Kyrillidis, and Volkan Cevher · 2015
Later among the works it cites.
Disco: Distributed optimization for self-concordant empirical loss
Yuchen Zhang and Xiao Lin · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Quoc Tran-Dinh, Anastasios Kyrillidis, and Volkan Cevher · 2013
Cited alongside, same era.
Saga: A fast incremental gradient method with support for non-strongly convex composite objectives
Aaron Defazio, Francis Bach, and Simon Lacoste-Julien · 2014
Cited alongside, same era.
Proximal Newton-type methods for minimizing composite functions
Jason D. Lee, Yuekai Sun, and Michael A. Saunders · 2014
Cited alongside, same era.
Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2014
Cited alongside, same era.
A proximal stochastic gradient method with progressive variance reduction
Lin Xiao and Tong Zhang · 2014
Cited alongside, same era.
Uniform sampling for matrix approximation
Michael B Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, and Aaron Sidford · 2015
Cited alongside, same era.
Katyusha: Accelerated variance reduction for faster sgd
Zeyuan Allen-Zhu
Cited in the paper.
Naman Agarwal, Brian Bullins, and Elad Hazan · 2016
Later among the works it cites.
A stochastic quasi-newton method for large-scale optimization
Richard H Byrd, Samantha L Hansen, Jorge Nocedal, and Yoram Singer · 2016
Later among the works it cites.
Inexact proximal newton methods for self-concordant functions
Jinchao Li, Martin S Andersen, and Lieven Vandenberghe · 2016
Later among the works it cites.
A linearly-convergent stochastic l-bfgs algorithm
Philipp Moritz, Robert Nishihara, and Michael I Jordan · 2016
Later among the works it cites.
Sub-sampled newton methods with non-uniform sampling
Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, and Michael W Mahoney · 2016
Later among the works it cites.
Revisiting sub-sampled newton methods
Haishan Ye, Luo Luo, and Zhihua Zhang · 2016
Later among the works it cites.