SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization

8 February 2015

Papers citing "SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization"

25 / 25 papers shown

Title
$Sketch-and-Project Meets Newton Method: Global $\mathcal O(k^{-2})$ Convergence with Low-Rank Updates$ Sketch-and-Project Meets Newton Method: Global $\mathcal O(k^{-2})$ Convergence with Low-Rank Updates Slavomír Hanzely 33 6 0 22 May 2023
Polynomial Preconditioning for Gradient Methods N. Doikov Anton Rodomanov 26 1 0 30 Jan 2023
A Randomised Subspace Gauss-Newton Method for Nonlinear Least-Squares C. Cartis J. Fowkes Zhen Shao 24 11 0 10 Nov 2022
ALS: Augmented Lagrangian Sketching Methods for Linear Systems M. Morshed 36 0 0 12 Aug 2022
SP2: A Second Order Stochastic Polyak Method Shuang Li W. Swartworth Martin Takávc Deanna Needell Robert Mansel Gower 29 13 0 17 Jul 2022
Augmented Newton Method for Optimization: Global Linear Rate and Momentum Interpretation M. Morshed ODL 29 1 0 23 May 2022
Stability and Generalization for Randomized Coordinate Descent Puyu Wang Liang Wu Yunwen Lei 27 7 0 17 Aug 2021
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality Jonathan Lacotte Yifei Wang Mert Pilanci 18 15 0 15 May 2021
Efficient Global Optimization of Non-differentiable, Symmetric Objectives for Multi Camera Placement Maria L. Hanel Carola-B. Schönlieb 17 10 0 20 Mar 2021
Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds Jonathan Lacotte Mert Pilanci 20 11 0 13 Dec 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark Schmidt Francis R. Bach Peter Richtárik 24 112 0 02 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 42 0 0 26 Aug 2020
Precise expressions for random projections: Low-rank approximation and randomized Newton Michal Derezinski Feynman T. Liang Zhenyu A. Liao Michael W. Mahoney 34 23 0 18 Jun 2020
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling Mojmír Mutný Michal Derezinski Andreas Krause 38 21 0 25 Oct 2019
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop D. Kovalev Samuel Horváth Peter Richtárik 36 155 0 24 Jan 2019
SEGA: Variance Reduction via Gradient Sketching Filip Hanzely Konstantin Mishchenko Peter Richtárik 25 71 0 09 Sep 2018
Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption Martin Bompaire Emmanuel Bacry Stéphane Gaïffas 25 6 0 10 Jul 2018
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 24 200 0 27 Dec 2017
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method Mark Eisen Aryan Mokhtari Alejandro Ribeiro 35 16 0 22 May 2017
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 71 1,877 0 08 Oct 2016
Randomized block proximal damped Newton method for composite self-concordant minimization Zhaosong Lu 22 11 0 01 Jul 2016
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Aryan Mokhtari Alejandro Ribeiro ODL 25 32 0 24 May 2016
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework Virginia Smith Simone Forte Michael I. Jordan Martin Jaggi 28 28 0 13 Dec 2015
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 93 737 0 19 Mar 2014
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 79 317 0 18 Feb 2014