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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
Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks Zhaoxian Wu Qing Ling Tianyi Chen G. Giannakis FedML AAML 117 186 0 29 Dec 2019
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Large-scale Kernel Methods and Applications to Lifelong Robot Learning Raffaello Camoriano 80 1 0 11 Dec 2019
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances Kai Zhang Zhuoran Yang Tamer Basar 71 68 0 09 Dec 2019
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Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features Shingo Yashima Atsushi Nitanda Taiji Suzuki 30 2 0 13 Nov 2019
Stochastic DCA for minimizing a large sum of DC functions with application to Multi-class Logistic Regression Hoai An Le Thi L. Minh Phan Duy Nhat Bach Tran 50 25 0 10 Nov 2019
Mixing of Stochastic Accelerated Gradient Descent Peiyuan Zhang Hadi Daneshmand Thomas Hofmann 22 0 0 31 Oct 2019
Katyusha Acceleration for Convex Finite-Sum Compositional Optimization Yibo Xu Yangyang Xu 128 13 0 24 Oct 2019
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 63 32 0 22 Oct 2019
A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization Minghan Yang Andre Milzarek Zaiwen Wen Tong Zhang ODL 96 36 0 21 Oct 2019
Improving the convergence of SGD through adaptive batch sizes Scott Sievert Zachary B. Charles ODL 63 8 0 18 Oct 2019
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Yujia Jin Aaron Sidford 51 7 0 15 Oct 2019
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning Sai Praneeth Karimireddy Satyen Kale M. Mohri Sashank J. Reddi Sebastian U. Stich A. Suresh FedML 75 348 0 14 Oct 2019
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme Tao Sun Yuejiao Sun Dongsheng Li Qing Liao 64 16 0 11 Oct 2019
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation S. Meng Sharan Vaswani I. Laradji Mark Schmidt Simon Lacoste-Julien 96 34 0 11 Oct 2019
Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip Nicolas Loizou 62 5 0 26 Sep 2019
A Stochastic Proximal Point Algorithm for Saddle-Point Problems Luo Luo Cheng Chen Yujun Li Guangzeng Xie Zhihua Zhang 142 16 0 13 Sep 2019
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Anis Elgabli Jihong Park Amrit Singh Bedi M. Bennis Vaneet Aggarwal FedML 83 83 0 30 Aug 2019
Linear Convergence of Adaptive Stochastic Gradient Descent Yuege Xie Xiaoxia Wu Rachel A. Ward 74 45 0 28 Aug 2019
Convex Programming for Estimation in Nonlinear Recurrent Models S. Bahmani Justin Romberg 57 10 0 26 Aug 2019
A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization Guangzeng Xie Luo Luo Zhihua Zhang 56 4 0 22 Aug 2019
Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Hao Jin Dachao Lin Zhihua Zhang ODL 35 2 0 18 Aug 2019
A Review of Cooperative Multi-Agent Deep Reinforcement Learning Afshin Oroojlooyjadid Davood Hajinezhad 120 436 0 11 Aug 2019
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods Ching-An Cheng Xinyan Yan Byron Boots 66 22 0 08 Aug 2019
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints Qihang Lin Selvaprabu Nadarajah Negar Soheili Tianbao Yang 85 13 0 07 Aug 2019
An introduction to decentralized stochastic optimization with gradient tracking Ran Xin S. Kar U. Khan 91 10 0 23 Jul 2019
Stochastic algorithms with geometric step decay converge linearly on sharp functions Damek Davis Dmitriy Drusvyatskiy Vasileios Charisopoulos 73 28 0 22 Jul 2019
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization Quoc Tran-Dinh Nhan H. Pham T. Dzung Lam M. Nguyen 78 51 0 08 Jul 2019
Variance Reduction for Matrix Games Y. Carmon Yujia Jin Aaron Sidford Kevin Tian 84 67 0 03 Jul 2019
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators Xun Zhang W. Haskell Z. Ye 42 3 0 22 Jun 2019
A Survey of Optimization Methods from a Machine Learning Perspective Shiliang Sun Zehui Cao Han Zhu Jing Zhao 82 566 0 17 Jun 2019
ADASS: Adaptive Sample Selection for Training Acceleration Shen-Yi Zhao Hao Gao Wu-Jun Li 43 0 0 11 Jun 2019
Variance-reduced $Q$ -learning is minimax optimal Martin J. Wainwright OffRL 84 93 0 11 Jun 2019
Reducing the variance in online optimization by transporting past gradients Sébastien M. R. Arnold Pierre-Antoine Manzagol Reza Babanezhad Ioannis Mitliagkas Nicolas Le Roux 81 28 0 08 Jun 2019
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1 Majid Jahani M. Nazari S. Rusakov A. Berahas Martin Takávc 75 14 0 30 May 2019
Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data Shicong Cen Huishuai Zhang Yuejie Chi Wei-neng Chen Tie-Yan Liu FedML 105 27 0 29 May 2019
A unified variance-reduced accelerated gradient method for convex optimization Guanghui Lan Zhize Li Yi Zhou 60 61 0 29 May 2019
Why gradient clipping accelerates training: A theoretical justification for adaptivity J.N. Zhang Tianxing He S. Sra Ali Jadbabaie 88 469 0 28 May 2019
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums Hadrien Hendrikx Francis R. Bach Laurent Massoulie 74 31 0 27 May 2019
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates Sharan Vaswani Aaron Mishkin I. Laradji Mark Schmidt Gauthier Gidel Simon Lacoste-Julien ODL 111 210 0 24 May 2019
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations Brian L. Trippe Jonathan H. Huggins Raj Agrawal Tamara Broderick BDL 65 9 0 17 May 2019
Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization Quoc Tran-Dinh Nhan H. Pham Dzung Phan Lam M. Nguyen 82 56 0 15 May 2019
A Stochastic Gradient Method with Biased Estimation for Faster Nonconvex Optimization Jia Bi S. Gunn 61 3 0 13 May 2019
Solving Empirical Risk Minimization in the Current Matrix Multiplication Time Y. Lee Zhao Song Qiuyi Zhang 104 117 0 11 May 2019
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks Jiong Zhang Hsiang-Fu Yu Inderjit S. Dhillon 56 27 0 08 May 2019
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization A. Kulunchakov Julien Mairal 74 27 0 07 May 2019
Differentiable Visual Computing Tzu-Mao Li 54 15 0 27 Apr 2019
Some Limit Properties of Markov Chains Induced by Stochastic Recursive Algorithms Abhishek Gupta Hao Chen Jianzong Pi Gaurav Tendolkar 28 0 0 24 Apr 2019
Reducing Noise in GAN Training with Variance Reduced Extragradient Tatjana Chavdarova Gauthier Gidel François Fleuret Simon Lacoste-Julien 71 137 0 18 Apr 2019