SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization

8 February 2015

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

28 / 28 papers shown

Title
Joker: Joint Optimization Framework for Lightweight Kernel Machines Junhong Zhang Zhihui Lai 48 0 0 23 May 2025
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling Mojmír Mutný Michal Derezinski Andreas Krause 100 21 0 25 Oct 2019
Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex Losses Dominik Csiba Peter Richtárik 77 23 0 07 Jun 2015
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting Jakub Konecný Jie Liu Peter Richtárik Martin Takáč ODL 108 273 0 16 Apr 2015
Coordinate Descent with Arbitrary Sampling II: Expected Separable Overapproximation Zheng Qu Peter Richtárik 175 83 0 27 Dec 2014
Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity Zheng Qu Peter Richtárik 72 130 0 27 Dec 2014
Randomized Dual Coordinate Ascent with Arbitrary Sampling Zheng Qu Peter Richtárik Tong Zhang 144 58 0 21 Nov 2014
Iterative Hessian sketch: Fast and accurate solution approximation for constrained least-squares Mert Pilanci Martin J. Wainwright 77 206 0 03 Nov 2014
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting Jakub Konecný Jie Liu Peter Richtárik Martin Takáč ODL 71 22 0 17 Oct 2014
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 135 1,828 0 01 Jul 2014
Fast Distributed Coordinate Descent for Non-Strongly Convex Losses Olivier Fercoq Zheng Qu Peter Richtárik Martin Takáč 88 59 0 21 May 2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 158 739 0 19 Mar 2014
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 149 319 0 18 Feb 2014
A Stochastic Quasi-Newton Method for Large-Scale Optimization R. Byrd Samantha Hansen J. Nocedal Y. Singer ODL 114 473 0 27 Jan 2014
Accelerated, Parallel and Proximal Coordinate Descent Olivier Fercoq Peter Richtárik 92 372 0 20 Dec 2013
Semi-Stochastic Gradient Descent Methods Jakub Konecný Peter Richtárik ODL 126 239 0 05 Dec 2013
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods Jascha Narain Sohl-Dickstein Ben Poole Surya Ganguli ODL 123 124 0 09 Nov 2013
On Optimal Probabilities in Stochastic Coordinate Descent Methods Peter Richtárik Martin Takáč 95 130 0 13 Oct 2013
Distributed Coordinate Descent Method for Learning with Big Data Peter Richtárik Martin Takáč 191 255 0 08 Oct 2013
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 324 1,250 0 10 Sep 2013
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang ODL 105 463 0 10 Sep 2013
Separable Approximations and Decomposition Methods for the Augmented Lagrangian R. Tappenden Peter Richtárik Burak Büke 89 41 0 30 Aug 2013
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent Shai Shalev-Shwartz Tong Zhang ODL 112 151 0 12 May 2013
Inexact Coordinate Descent: Complexity and Preconditioning R. Tappenden Peter Richtárik J. Gondzio 89 100 0 19 Apr 2013
Mini-Batch Primal and Dual Methods for SVMs Martin Takáč A. Bijral Peter Richtárik Nathan Srebro 72 196 0 10 Mar 2013
Parallel Coordinate Descent Methods for Big Data Optimization Peter Richtárik Martin Takáč 127 487 0 04 Dec 2012
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang 190 1,033 0 10 Sep 2012
Iteration Complexity of Randomized Block-Coordinate Descent Methods for Minimizing a Composite Function Peter Richtárik Martin Takáč 98 772 0 14 Jul 2011