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
Gradient-Based Empirical Risk Minimization using Local Polynomial Regression Ali Jadbabaie A. Makur Devavrat Shah 28 6 0 04 Nov 2020
Local SGD: Unified Theory and New Efficient Methods Eduard A. Gorbunov Filip Hanzely Peter Richtárik FedML 35 109 0 03 Nov 2020
Asynchronous Parallel Stochastic Quasi-Newton Methods Qianqian Tong Guannan Liang Xingyu Cai Chunjiang Zhu J. Bi ODL 24 9 0 02 Nov 2020
Tight Lower Complexity Bounds for Strongly Convex Finite-Sum Optimization Min Zhang Yao Shu Kun He 13 1 0 17 Oct 2020
Structured Logconcave Sampling with a Restricted Gaussian Oracle Y. Lee Ruoqi Shen Kevin Tian 6 68 0 07 Oct 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark W. Schmidt Francis R. Bach Peter Richtárik 19 111 0 02 Oct 2020
A variable metric mini-batch proximal stochastic recursive gradient algorithm with diagonal Barzilai-Borwein stepsize Tengteng Yu Xinwei Liu Yuhong Dai Jie Sun 10 4 0 02 Oct 2020
Understanding the Role of Adversarial Regularization in Supervised Learning Litu Rout 12 3 0 01 Oct 2020
Projection-Free Adaptive Gradients for Large-Scale Optimization Cyrille W. Combettes Christoph Spiegel Sebastian Pokutta ODL 18 10 0 29 Sep 2020
Cross Learning in Deep Q-Networks Xing Wang A. Vinel 9 2 0 29 Sep 2020
A general framework for decentralized optimization with first-order methods Ran Xin Shi Pu Angelia Nedić U. Khan 16 87 0 12 Sep 2020
Neural Neighborhood Encoding for Classification Kaushik Sinha Parikshit Ram 25 1 0 19 Aug 2020
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning Bin Gu An Xu Zhouyuan Huo Cheng Deng Heng-Chiao Huang FedML 38 27 0 14 Aug 2020
A Survey on Large-scale Machine Learning Meng Wang Weijie Fu Xiangnan He Shijie Hao Xindong Wu 19 109 0 10 Aug 2020
On the Regularization Effect of Stochastic Gradient Descent applied to Least Squares Stefan Steinerberger 9 1 0 27 Jul 2020
Langevin Monte Carlo: random coordinate descent and variance reduction Zhiyan Ding Qin Li BDL 17 11 0 26 Jul 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou Hugo Berard Alexia Jolicoeur-Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas 39 50 0 08 Jul 2020
Conditional gradient methods for stochastically constrained convex minimization Maria-Luiza Vladarean Ahmet Alacaoglu Ya-Ping Hsieh V. Cevher 6 3 0 07 Jul 2020
Streaming Complexity of SVMs Alexandr Andoni Collin Burns Yi Li S. Mahabadi David P. Woodruff 17 5 0 07 Jul 2020
Stochastic Stein Discrepancies Jackson Gorham Anant Raj Lester W. Mackey 30 37 0 06 Jul 2020
A Multilevel Approach to Training Vanessa Braglia Alena Kopanicáková Rolf Krause 10 2 0 28 Jun 2020
Dual-Free Stochastic Decentralized Optimization with Variance Reduction Hadrien Hendrikx Francis R. Bach Laurent Massoulié 11 25 0 25 Jun 2020
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization Ahmed Khaled Othmane Sebbouh Nicolas Loizou Robert Mansel Gower Peter Richtárik 11 46 0 20 Jun 2020
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation Robert Mansel Gower Othmane Sebbouh Nicolas Loizou 25 74 0 18 Jun 2020
AdaS: Adaptive Scheduling of Stochastic Gradients Mahdi S. Hosseini Konstantinos N. Plataniotis ODL 23 12 0 11 Jun 2020
Variance reduction for Random Coordinate Descent-Langevin Monte Carlo Zhiyan Ding Qin Li BDL 15 0 0 10 Jun 2020
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm Majid Jahani M. Nazari R. Tappenden A. Berahas Martin Takávc ODL 11 10 0 06 Jun 2020
Improved SVRG for quadratic functions N. Kahalé 17 0 0 01 Jun 2020
CoolMomentum: A Method for Stochastic Optimization by Langevin Dynamics with Simulated Annealing O. Borysenko M. Byshkin ODL 19 14 0 29 May 2020
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates Kaiwen Zhou Anthony Man-Cho So James Cheng 14 5 0 25 May 2020
An Optimal Algorithm for Decentralized Finite Sum Optimization Hadrien Hendrikx Francis R. Bach Laurent Massoulie 9 45 0 20 May 2020
A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning Shijun Wang Baocheng Zhu Lintao Ma Yuan Qi 8 0 0 19 May 2020
The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent Xin-Yao Qian Diego Klabjan ODL 13 35 0 27 Apr 2020
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan H. Zha 25 21 0 17 Apr 2020
Analysis of Stochastic Gradient Descent in Continuous Time J. Latz 33 38 0 15 Apr 2020
Communication-efficient Variance-reduced Stochastic Gradient Descent H. S. Ghadikolaei Sindri Magnússon 4 3 0 10 Mar 2020
Buffered Asynchronous SGD for Byzantine Learning Yi-Rui Yang Wu-Jun Li FedML 29 5 0 02 Mar 2020
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization Geoffrey Negiar Gideon Dresdner Alicia Y. Tsai L. Ghaoui Francesco Locatello Robert M. Freund Fabian Pedregosa 6 24 0 27 Feb 2020
General Framework for Binary Classification on Top Samples Lukáš Adam V. Mácha Václav Smídl Tomás Pevný 29 5 0 25 Feb 2020
$Can speed up the convergence rate of stochastic gradient methods to $\mathcal{O}(1/k^2)$ by a gradient averaging strategy?$ Can speed up the convergence rate of stochastic gradient methods to $\mathcal{O}(1/k^2)$ by a gradient averaging strategy? Xin Xu Xiaopeng Luo 11 1 0 25 Feb 2020
Statistical Adaptive Stochastic Gradient Methods Pengchuan Zhang Hunter Lang Qiang Liu Lin Xiao ODL 15 11 0 25 Feb 2020
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients Ruilin Li Xin Wang H. Zha Molei Tao 10 4 0 20 Feb 2020
Sampling and Update Frequencies in Proximal Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 27 4 0 13 Feb 2020
Gradient tracking and variance reduction for decentralized optimization and machine learning Ran Xin S. Kar U. Khan 19 10 0 13 Feb 2020
On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions Yossi Arjevani Amit Daniely Stefanie Jegelka Hongzhou Lin 19 2 0 09 Feb 2020
Adaptive Stochastic Optimization Frank E. Curtis K. Scheinberg ODL 13 29 0 18 Jan 2020
Stochastic Recursive Variance Reduction for Efficient Smooth Non-Convex Compositional Optimization Huizhuo Yuan Xiangru Lian Ji Liu 19 13 0 31 Dec 2019
Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks Zhaoxian Wu Qing Ling Tianyi Chen G. Giannakis FedML AAML 32 181 0 29 Dec 2019
Comparison of Classification Methods for Very High-Dimensional Data in Sparse Random Projection Representation Anton Akusok Emil Eirola 17 1 0 18 Dec 2019
Large-scale Kernel Methods and Applications to Lifelong Robot Learning Raffaello Camoriano 29 1 0 11 Dec 2019