Minimizing Finite Sums with the Stochastic Average Gradient

10 September 2013

Nicolas Le Roux

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

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Title
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods Robert Mansel Gower Nicolas Le Roux Francis R. Bach 11 31 0 20 Oct 2017
Smooth and Sparse Optimal Transport Mathieu Blondel Vivien Seguy Antoine Rolet OT 21 171 0 17 Oct 2017
Nesterov's Acceleration For Approximate Newton Haishan Ye Zhihua Zhang ODL 9 13 0 17 Oct 2017
Sign-Constrained Regularized Loss Minimization Tsuyoshi Kato Misato Kobayashi Daisuke Sano 20 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 19 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 36 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 13 83 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 19 28 0 04 Sep 2017
Natasha 2: Faster Non-Convex Optimization Than SGD Zeyuan Allen-Zhu ODL 28 245 0 29 Aug 2017
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information Peng Xu Farbod Roosta-Khorasani Michael W. Mahoney 26 210 0 23 Aug 2017
Variance-Reduced Stochastic Learning by Networked Agents under Random Reshuffling Kun Yuan Bicheng Ying Jiageng Liu Ali H. Sayed 16 4 0 04 Aug 2017
A Robust Multi-Batch L-BFGS Method for Machine Learning A. Berahas Martin Takáč AAML ODL 17 44 0 26 Jul 2017
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization Fabian Pedregosa Rémi Leblond Simon Lacoste-Julien 21 34 0 20 Jul 2017
Stochastic Variance Reduction Gradient for a Non-convex Problem Using Graduated Optimization Li Chen Shuisheng Zhou Zhuan Zhang 16 3 0 10 Jul 2017
Stochastic, Distributed and Federated Optimization for Machine Learning Jakub Konecný FedML 26 38 0 04 Jul 2017
Generalization Properties of Doubly Stochastic Learning Algorithms Junhong Lin Lorenzo Rosasco 29 7 0 03 Jul 2017
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning Frank E. Curtis K. Scheinberg 39 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 30 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 38 184 0 15 Jun 2017
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sum Optimization Yossi Arjevani 18 12 0 06 Jun 2017
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory Peter Richtárik Martin Takáč 17 92 0 04 Jun 2017
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration Jason M. Altschuler Jonathan Niles-Weed Philippe Rigollet OT 23 583 0 26 May 2017
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization Lam M. Nguyen Jie Liu K. Scheinberg Martin Takáč 11 94 0 20 May 2017
Nestrov's Acceleration For Second Order Method Haishan Ye Zhihua Zhang ODL 11 4 0 19 May 2017
An Investigation of Newton-Sketch and Subsampled Newton Methods A. Berahas Raghu Bollapragada J. Nocedal 19 111 0 17 May 2017
Determinantal Point Processes for Mini-Batch Diversification Cheng Zhang Hedvig Kjellström Stephan Mandt 19 35 0 01 May 2017
Limits of End-to-End Learning Tobias Glasmachers 11 155 0 26 Apr 2017
Batch-Expansion Training: An Efficient Optimization Framework Michal Derezinski D. Mahajan S. Keerthi S.V.N. Vishwanathan Markus Weimer 6 6 0 22 Apr 2017
Importance Sampled Stochastic Optimization for Variational Inference J. Sakaya Arto Klami BDL 11 7 0 19 Apr 2017
Larger is Better: The Effect of Learning Rates Enjoyed by Stochastic Optimization with Progressive Variance Reduction Fanhua Shang 9 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 27 153 0 17 Apr 2017
Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies Renbo Zhao W. Haskell Vincent Y. F. Tan 12 29 0 01 Apr 2017
Catalyst Acceleration for Gradient-Based Non-Convex Optimization Courtney Paquette Hongzhou Lin Dmitriy Drusvyatskiy Julien Mairal Zaïd Harchaoui ODL 21 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 16 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 14 3 0 20 Mar 2017
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization Tomoya Murata Taiji Suzuki OffRL 33 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 28 597 0 01 Mar 2017
SAGA and Restricted Strong Convexity C. Qu Yan Li Huan Xu 20 5 0 19 Feb 2017
Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter Zeyuan Allen-Zhu 17 80 0 02 Feb 2017
IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate Aryan Mokhtari Mark Eisen Alejandro Ribeiro 25 73 0 02 Feb 2017
Linear convergence of SDCA in statistical estimation C. Qu Huan Xu 43 8 0 26 Jan 2017
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery Lingxiao Wang Xiao Zhang Quanquan Gu 27 11 0 09 Jan 2017
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements Xiao Zhang Lingxiao Wang Quanquan Gu 20 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 21 79 0 13 Dec 2016
Subsampled online matrix factorization with convergence guarantees A. Mensch Julien Mairal Gaël Varoquaux B. Thirion 11 2 0 30 Nov 2016
Accelerated Variance Reduced Block Coordinate Descent Zebang Shen Hui Qian Chao Zhang Tengfei Zhou 27 1 0 13 Nov 2016
Linear Convergence of SVRG in Statistical Estimation C. Qu Yan Li Huan Xu 16 11 0 07 Nov 2016
Finding Approximate Local Minima Faster than Gradient Descent Naman Agarwal Zeyuan Allen-Zhu Brian Bullins Elad Hazan Tengyu Ma 33 83 0 03 Nov 2016
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate Aryan Mokhtari Mert Gurbuzbalaban Alejandro Ribeiro 27 36 0 01 Nov 2016
Asynchronous Stochastic Block Coordinate Descent with Variance Reduction Bin Gu Zhouyuan Huo Heng-Chiao Huang 23 10 0 29 Oct 2016