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
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 20 2 0 21 Mar 2019
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework Arman Rahbar Ashkan Panahi M. Chehreghani Devdatt Dubhashi Hamid Krim 23 0 0 09 Mar 2019
SGD without Replacement: Sharper Rates for General Smooth Convex Functions Prateek Jain Dheeraj M. Nagaraj Praneeth Netrapalli 11 87 0 04 Mar 2019
Stochastic Conditional Gradient++ Hamed Hassani Amin Karbasi Aryan Mokhtari Zebang Shen 8 22 0 19 Feb 2019
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization Nhan H. Pham Lam M. Nguyen Dzung Phan Quoc Tran-Dinh 11 139 0 15 Feb 2019
Do Subsampled Newton Methods Work for High-Dimensional Data? Xiang Li Shusen Wang Zhihua Zhang 13 12 0 13 Feb 2019
Efficient Primal-Dual Algorithms for Large-Scale Multiclass Classification Dmitry Babichev Dmitrii Ostrovskii Francis R. Bach VLM 18 3 0 11 Feb 2019
A Smoother Way to Train Structured Prediction Models Krishna Pillutla Vincent Roulet Sham Kakade Zaïd Harchaoui 17 19 0 08 Feb 2019
Momentum Schemes with Stochastic Variance Reduction for Nonconvex Composite Optimization Yi Zhou Zhe Wang Kaiyi Ji Yingbin Liang Vahid Tarokh ODL 41 14 0 07 Feb 2019
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions Adrien B. Taylor Francis R. Bach 6 60 0 03 Feb 2019
Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions Yunwen Lei Ting Hu Guiying Li K. Tang MLT 13 115 0 03 Feb 2019
Sharp Analysis for Nonconvex SGD Escaping from Saddle Points Cong Fang Zhouchen Lin Tong Zhang 15 103 0 01 Feb 2019
Optimal mini-batch and step sizes for SAGA Nidham Gazagnadou Robert Mansel Gower Joseph Salmon 19 34 0 31 Jan 2019
Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample A. Berahas Majid Jahani Peter Richtárik Martin Takávc 21 40 0 28 Jan 2019
Asynchronous Accelerated Proximal Stochastic Gradient for Strongly Convex Distributed Finite Sums Hadrien Hendrikx Francis R. Bach Laurent Massoulié FedML 8 26 0 28 Jan 2019
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it Konstantin Mishchenko Filip Hanzely Peter Richtárik 16 13 0 27 Jan 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 29 44 0 25 Jan 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 30 155 0 24 Jan 2019
SAGA with Arbitrary Sampling Xun Qian Zheng Qu Peter Richtárik 34 25 0 24 Jan 2019
Trajectory Normalized Gradients for Distributed Optimization Jianqiao Wangni Ke Li Jianbo Shi Jitendra Malik 11 2 0 24 Jan 2019
Finite-Sum Smooth Optimization with SARAH Lam M. Nguyen Marten van Dijk Dzung Phan Phuong Ha Nguyen Tsui-Wei Weng Jayant Kalagnanam 28 22 0 22 Jan 2019
DTN: A Learning Rate Scheme with Convergence Rate of $\mathcal{O}(1/t)$ for SGD Lam M. Nguyen Phuong Ha Nguyen Dzung Phan Jayant Kalagnanam Marten van Dijk 25 0 0 22 Jan 2019
Quantized Epoch-SGD for Communication-Efficient Distributed Learning Shen-Yi Zhao Hao Gao Wu-Jun Li FedML 14 3 0 10 Jan 2019
The Lingering of Gradients: Theory and Applications Zeyuan Allen-Zhu D. Simchi-Levi Xinshang Wang 13 4 0 09 Jan 2019
SGD Converges to Global Minimum in Deep Learning via Star-convex Path Yi Zhou Junjie Yang Huishuai Zhang Yingbin Liang Vahid Tarokh 14 71 0 02 Jan 2019
A continuous-time analysis of distributed stochastic gradient Nicholas M. Boffi Jean-Jacques E. Slotine 20 15 0 28 Dec 2018
Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning Vinod Kumar Chauhan A. Sharma Kalpana Dahiya 7 6 0 26 Dec 2018
Tight Analyses for Non-Smooth Stochastic Gradient Descent Nicholas J. A. Harvey Christopher Liaw Y. Plan Sikander Randhawa 19 136 0 13 Dec 2018
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning Aaron Defazio Léon Bottou UQCV DRL 23 112 0 11 Dec 2018
Inexact SARAH Algorithm for Stochastic Optimization Lam M. Nguyen K. Scheinberg Martin Takáč 6 50 0 25 Nov 2018
Asynchronous Stochastic Composition Optimization with Variance Reduction Shuheng Shen Linli Xu Jingchang Liu Junliang Guo Qing Ling 6 2 0 15 Nov 2018
R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate J.N. Zhang Hongyi Zhang S. Sra 26 39 0 10 Nov 2018
Machine Learning Methods for Track Classification in the AT-TPC M. Kuchera R. Ramanujan Jack Z. Taylor R. Strauss D. Bazin J. Bradt Ruiming Chen 16 32 0 21 Oct 2018
Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates Lucas Cassano Kun Yuan Ali H. Sayed 19 39 0 17 Oct 2018
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron Sharan Vaswani Francis R. Bach Mark W. Schmidt 30 296 0 16 Oct 2018
Quasi-hyperbolic momentum and Adam for deep learning Jerry Ma Denis Yarats ODL 84 129 0 16 Oct 2018
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras Yeqi Liu Yingyi Chen Huihui Yu X. Fang Chuanyang Gong 19 2 0 09 Oct 2018
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD Marten van Dijk Lam M. Nguyen Phuong Ha Nguyen Dzung Phan 36 6 0 09 Oct 2018
ASVRG: Accelerated Proximal SVRG Fanhua Shang L. Jiao Kaiwen Zhou James Cheng Yan Ren Yufei Jin ODL 21 30 0 07 Oct 2018
A fast quasi-Newton-type method for large-scale stochastic optimisation A. Wills Carl Jidling Thomas B. Schon ODL 23 7 0 29 Sep 2018
Sparsified SGD with Memory Sebastian U. Stich Jean-Baptiste Cordonnier Martin Jaggi 41 739 0 20 Sep 2018
Quantum Algorithms for Structured Prediction Behrooz Sepehry E. Iranmanesh M. Friedlander Pooya Ronagh 11 2 0 11 Sep 2018
Compositional Stochastic Average Gradient for Machine Learning and Related Applications Tsung-Yu Hsieh Y. El-Manzalawy Yiwei Sun Vasant Honavar 13 1 0 04 Sep 2018
Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks Nikola B. Kovachki Andrew M. Stuart BDL 42 136 0 10 Aug 2018
Fast Variance Reduction Method with Stochastic Batch Size Xuanqing Liu Cho-Jui Hsieh 15 5 0 07 Aug 2018
Efficient Training on Very Large Corpora via Gramian Estimation Walid Krichene Nicolas Mayoraz Steffen Rendle Li Zhang Xinyang Yi Lichan Hong Ed H. Chi John R. Anderson 6 47 0 18 Jul 2018
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches Jie Liu Yu Rong Martin Takáč Junzhou Huang ODL 22 7 0 14 Jul 2018
Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption Martin Bompaire Emmanuel Bacry Stéphane Gaïffas 17 6 0 10 Jul 2018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator Cong Fang C. J. Li Zhouchen Lin Tong Zhang 44 570 0 04 Jul 2018
Direct Acceleration of SAGA using Sampled Negative Momentum Kaiwen Zhou 8 45 0 28 Jun 2018