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Minimizing Finite Sums with the Stochastic Average Gradient

10 September 2013

Mark Schmidt

Nicolas Le Roux

Francis R. Bach

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Papers citing "Minimizing Finite Sums with the Stochastic Average Gradient"

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Title
Duality-free Methods for Stochastic Composition Optimization Liu Liu Ji Liu Dacheng Tao 73 16 0 26 Oct 2017
Optimal Rates for Learning with Nyström Stochastic Gradient Methods Junhong Lin Lorenzo Rosasco 90 7 0 21 Oct 2017
A Novel Stochastic Stratified Average Gradient Method: Convergence Rate and Its Complexity Aixiang Chen Bingchuan Chen Xiaolong Chai Rui-Ling Bian Hengguang Li 65 22 0 21 Oct 2017
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods Robert Mansel Gower Nicolas Le Roux Francis R. Bach 49 31 0 20 Oct 2017
Smooth and Sparse Optimal Transport Mathieu Blondel Vivien Seguy Antoine Rolet OT 96 175 0 17 Oct 2017
Nesterov's Acceleration For Approximate Newton Haishan Ye Zhihua Zhang ODL 45 13 0 17 Oct 2017
Sign-Constrained Regularized Loss Minimization Tsuyoshi Kato Misato Kobayashi Daisuke Sano 36 0 0 12 Oct 2017
Fast and Safe: Accelerated gradient methods with optimality certificates and underestimate sequences Majid Jahani N. V. C. Gudapati Chenxin Ma R. Tappenden Martin Takáč 59 7 0 10 Oct 2017
SGD for robot motion? The effectiveness of stochastic optimization on a new benchmark for biped locomotion tasks Martim Brandao K. Hashimoto A. Takanishi 60 6 0 09 Oct 2017
A Generic Approach for Escaping Saddle points Sashank J. Reddi Manzil Zaheer S. Sra Barnabás Póczós Francis R. Bach Ruslan Salakhutdinov Alex Smola 120 84 0 05 Sep 2017
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC Changyou Chen Wenlin Wang Yizhe Zhang Qinliang Su Lawrence Carin 94 28 0 04 Sep 2017
Natasha 2: Faster Non-Convex Optimization Than SGD Zeyuan Allen-Zhu ODL 106 246 0 29 Aug 2017
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information Peng Xu Farbod Roosta-Khorasani Michael W. Mahoney 131 214 0 23 Aug 2017
Variance-Reduced Stochastic Learning by Networked Agents under Random Reshuffling Kun Yuan Bicheng Ying Jiageng Liu Ali H. Sayed 56 4 0 04 Aug 2017
A Robust Multi-Batch L-BFGS Method for Machine Learning A. Berahas Martin Takáč AAML ODL 111 44 0 26 Jul 2017
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization Fabian Pedregosa Rémi Leblond Simon Lacoste-Julien 86 34 0 20 Jul 2017
Stochastic Variance Reduction Gradient for a Non-convex Problem Using Graduated Optimization Li Chen Shuisheng Zhou Zhuan Zhang 46 3 0 10 Jul 2017
Stochastic, Distributed and Federated Optimization for Machine Learning Jakub Konecný FedML 75 38 0 04 Jul 2017
Generalization Properties of Doubly Stochastic Learning Algorithms Junhong Lin Lorenzo Rosasco 62 7 0 03 Jul 2017
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning Frank E. Curtis K. Scheinberg 98 45 0 30 Jun 2017
A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints Bin Hu Peter M. Seiler Anders Rantzer 121 35 0 25 Jun 2017
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications A. Chambolle Matthias Joachim Ehrhardt Peter Richtárik Carola-Bibiane Schönlieb 104 187 0 15 Jun 2017
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sum Optimization Yossi Arjevani 63 12 0 06 Jun 2017
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory Peter Richtárik Martin Takáč 92 94 0 04 Jun 2017
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration Jason M. Altschuler Jonathan Niles-Weed Philippe Rigollet OT 93 596 0 26 May 2017
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization Lam M. Nguyen Jie Liu K. Scheinberg Martin Takáč 75 94 0 20 May 2017
Nestrov's Acceleration For Second Order Method Haishan Ye Zhihua Zhang ODL 32 4 0 19 May 2017
An Investigation of Newton-Sketch and Subsampled Newton Methods A. Berahas Raghu Bollapragada J. Nocedal 104 114 0 17 May 2017
Determinantal Point Processes for Mini-Batch Diversification Cheng Zhang Hedvig Kjellström Stephan Mandt 80 35 0 01 May 2017
Limits of End-to-End Learning Tobias Glasmachers 68 158 0 26 Apr 2017
Batch-Expansion Training: An Efficient Optimization Framework Michal Derezinski D. Mahajan S. Keerthi S.V.N. Vishwanathan Markus Weimer 37 6 0 22 Apr 2017
Importance Sampled Stochastic Optimization for Variational Inference J. Sakaya Arto Klami BDL 48 7 0 19 Apr 2017
Larger is Better: The Effect of Learning Rates Enjoyed by Stochastic Optimization with Progressive Variance Reduction Fanhua Shang 21 1 0 17 Apr 2017
Deep Relaxation: partial differential equations for optimizing deep neural networks Pratik Chaudhari Adam M. Oberman Stanley Osher Stefano Soatto G. Carlier 163 154 0 17 Apr 2017
Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies Renbo Zhao W. Haskell Vincent Y. F. Tan 53 29 0 01 Apr 2017
Catalyst Acceleration for Gradient-Based Non-Convex Optimization Courtney Paquette Hongzhou Lin Dmitriy Drusvyatskiy Julien Mairal Zaïd Harchaoui ODL 76 40 0 31 Mar 2017
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning Fanhua Shang Yuanyuan Liu James Cheng Jiacheng Zhuo ODL 51 23 0 23 Mar 2017
Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent Fanhua Shang Yuanyuan Liu James Cheng K. K. Ng Yuichi Yoshida 98 3 0 20 Mar 2017
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization Tomoya Murata Taiji Suzuki OffRL 124 28 0 01 Mar 2017
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient Lam M. Nguyen Jie Liu K. Scheinberg Martin Takáč ODL 175 607 0 01 Mar 2017
SAGA and Restricted Strong Convexity Chao Qu Yan Li Huan Xu 51 5 0 19 Feb 2017
Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter Zeyuan Allen-Zhu 110 80 0 02 Feb 2017
IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate Aryan Mokhtari Mark Eisen Alejandro Ribeiro 98 74 0 02 Feb 2017
Linear convergence of SDCA in statistical estimation Chao Qu Huan Xu 75 8 0 26 Jan 2017
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery Lingxiao Wang Xiao Zhang Quanquan Gu 89 11 0 09 Jan 2017
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements Xiao Zhang Lingxiao Wang Quanquan Gu 70 6 0 02 Jan 2017
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space Alec Koppel Garrett A. Warnell Ethan Stump Alejandro Ribeiro 70 79 0 13 Dec 2016
Subsampled online matrix factorization with convergence guarantees A. Mensch Julien Mairal Gaël Varoquaux Bertrand Thirion 20 2 0 30 Nov 2016
Accelerated Variance Reduced Block Coordinate Descent Zebang Shen Hui Qian Chao Zhang Tengfei Zhou 44 1 0 13 Nov 2016
Linear Convergence of SVRG in Statistical Estimation Chao Qu Yan Li Huan Xu 76 11 0 07 Nov 2016