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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DSAG: A mixed synchronous-asynchronous iterative method for straggler-resilient learning A. Severinson E. Rosnes S. E. Rouayheb Alexandre Graell i Amat 22 2 0 27 Nov 2021
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Nearly Optimal Linear Convergence of Stochastic Primal-Dual Methods for Linear Programming Haihao Lu Jinwen Yang 22 6 0 10 Nov 2021
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Fast Line Search for Multi-Task Learning A. Filatov D. Merkulov 22 0 0 02 Oct 2021
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou Anthony Man-Cho So James Cheng 27 1 0 30 Sep 2021
Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features Bruce W. Lee Yoonna Jang J. Lee VLM 48 75 0 25 Sep 2021
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Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees Hamid Reza Feyzmahdavian M. Johansson 32 20 0 09 Sep 2021
COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization Manuel Madeira Renato M. P. Negrinho J. Xavier P. Aguiar 26 0 0 07 Sep 2021
Anarchic Federated Learning Haibo Yang Xin Zhang Prashant Khanduri Jia Liu FedML 24 58 0 23 Aug 2021
Decentralized Composite Optimization with Compression Yao Li Xiaorui Liu Jiliang Tang Ming Yan Kun Yuan 27 9 0 10 Aug 2021
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Coordinate-wise Control Variates for Deep Policy Gradients Yuanyi Zhong Yuanshuo Zhou Jian-wei Peng BDL 30 1 0 11 Jul 2021
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The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence Daogao Liu Zhou Lu LRM 24 1 0 28 Jun 2021
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Adaptive Learning Rate and Momentum for Training Deep Neural Networks Zhiyong Hao Yixuan Jiang Huihua Yu H. Chiang ODL 24 9 0 22 Jun 2021
Kernel Clustering with Sigmoid-based Regularization for Efficient Segmentation of Sequential Data Tung Doan Atsuhiro Takasu 43 1 0 22 Jun 2021
Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processing Carlos Basto 16 5 0 25 May 2021
Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders Xinmeng Huang Kun Yuan Xianghui Mao W. Yin 27 12 0 25 Apr 2021
Generalization of GANs and overparameterized models under Lipschitz continuity Khoat Than Nghia D. Vu AI4CE 28 2 0 06 Apr 2021
Stochastic Reweighted Gradient Descent Ayoub El Hanchi D. Stephens 27 8 0 23 Mar 2021
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Zhize Li 48 14 0 21 Mar 2021
Piecewise linear regression and classification Alberto Bemporad 19 4 0 10 Mar 2021
A Retrospective Approximation Approach for Smooth Stochastic Optimization David Newton Raghu Bollapragada R. Pasupathy N. Yip 35 2 0 07 Mar 2021
Learning with Smooth Hinge Losses Junru Luo Hong Qiao Bo Zhang 18 20 0 27 Feb 2021
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods Zheng Shi Abdurakhmon Sadiev Nicolas Loizou Peter Richtárik Martin Takávc ODL 36 13 0 19 Feb 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark Schmidt Simon Lacoste-Julien 23 18 0 18 Feb 2021
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method Junyu Zhang Chengzhuo Ni Zheng Yu Csaba Szepesvári Mengdi Wang 66 67 0 17 Feb 2021
Distributed Second Order Methods with Fast Rates and Compressed Communication Rustem Islamov Xun Qian Peter Richtárik 34 51 0 14 Feb 2021
Federated Learning on Non-IID Data Silos: An Experimental Study Yue Liu Yiqun Diao Quan Chen Bingsheng He FedML OOD 101 955 0 03 Feb 2021
Variational Neural Annealing Mohamed Hibat-Allah E. Inack R. Wiersema R. Melko Juan Carrasquilla DRL 19 80 0 25 Jan 2021
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Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications Xiang Li Zhihua Zhang 30 4 0 05 Jan 2021
Learning Sign-Constrained Support Vector Machines Kenya Tajima Takahiko Henmi Kohei Tsuchida E. R. R. Zara Tsuyoshi Kato 14 1 0 05 Jan 2021
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization Jinshan Zeng Yixuan Zha Ke Ma Yuan Yao 11 0 0 01 Jan 2021
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction Haishan Ye Wei Xiong Tong Zhang 16 16 0 30 Dec 2020
Fast Incremental Expectation Maximization for finite-sum optimization: nonasymptotic convergence G. Fort Pierre Gach Eric Moulines 15 9 0 29 Dec 2020
Stochastic Gradient Variance Reduction by Solving a Filtering Problem Xingyi Yang 16 2 0 22 Dec 2020
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? Antoine Grosnit Alexander I. Cowen-Rivers Rasul Tutunov Ryan-Rhys Griffiths Jun Wang Haitham Bou-Ammar 19 13 0 15 Dec 2020
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SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition Hao Li Zixuan Li KenLi Li Jan S. Rellermeyer L. Chen Keqin Li 24 7 0 07 Dec 2020
Characterization of Excess Risk for Locally Strongly Convex Population Risk Mingyang Yi Ruoyu Wang Zhi-Ming Ma 14 2 0 04 Dec 2020
$Convergence of Gradient Algorithms for Nonconvex C^{1+alpha} Cost Functions$ Convergence of Gradient Algorithms for Nonconvex C^{1+alpha} Cost Functions Zixuan Wang Shanjian Tang 13 0 0 01 Dec 2020
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