Fetching the paper…
Reading the bibliography…
Many machine learning models involve solving optimization problems.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
Earlier work this paper cites.
Iterative solution of nonlinear equations in several variables , volume 30
James M Ortega and Werner C Rheinboldt · 1970
Earlier work this paper cites.
Extensions of Lipschitz mappings into a Hilbert space
William B. Johnson and Joram Lindenstrauss · 1984
Earlier work this paper cites.
Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
Earlier work this paper cites.
Sampling algorithms for l 2 regression and applications
Petros Drineas, Michael W Mahoney, and S Muthukrishnan · 2006
Earlier work this paper cites.
Numerical optimization
Jorge Nocedal and Stephen Wright · 2006
Earlier work this paper cites.
Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro · 2009
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.
Better mini-batch algorithms via accelerated gradient methods
Andrew Cotter, Ohad Shamir, Nati Srebro, and Karthik Sridharan · 2011
Earlier work this paper cites.
Finding Structure with Randomness : Probabilistic Algorithms for Matrix Decompositions
N Halko, P G Martinsson, and J A Tropp · 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
Cited alongside, same era.
Matrix analysis
Roger A Horn and Charles R Johnson · 2012
Cited alongside, same era.
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.
Low rank approximation and regression in input sparsity time
Kenneth L Clarkson and David P Woodruff · 2013
Cited alongside, same era.
Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
Cited alongside, same era.
Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression
Xiangrui Meng and Michael W Mahoney · 2013
Efficient mini-batch training for stochastic optimization
Mu Li, Tong Zhang, Yuqiang Chen, and Alexander J Smola · 2014
Later among the works it cites.
Sketching as a tool for numerical linear algebra
David P Woodruff · 2014
Later among the works it cites.
Convergence rates of sub-sampled newton methods
Murat A Erdogdu and Andrea Montanari · 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.
An introduction to matrix concentration inequalities
Joel A Tropp et al · 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…
Cited alongside, same era.
Osnap: Faster numerical linear algebra algorithms via sparser subspace embeddings
Jelani Nelson and Huy L Nguyên · 2013
Cited alongside, same era.
Minimizing finite sums with the stochastic average gradient
Mark Schmidt, Nicolas Le Roux, and Francis Bach · 2013
Cited alongside, same era.
Linear convergence with condition number independent access of full gradients
Lijun Zhang, Mehrdad Mahdavi, and Rong Jin · 2013
Cited alongside, same era.
Naman Agarwal, Brian Bullins, and Elad Hazan · 2016
Later among the works it cites.
Sub-sampled newton methods ii: Local convergence rates
Farbod Roosta-Khorasani and Michael W Mahoney · 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.
Sub-sampled newton methods
Farbod Roosta-Khorasani and Michael W Mahoney · 2019
Closest in time.