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
Random-reshuffled SARAH does not need a full gradient computations Aleksandr Beznosikov Martin Takáč 26 7 0 26 Nov 2021
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Linear Speedup in Personalized Collaborative Learning El Mahdi Chayti Sai Praneeth Karimireddy Sebastian U. Stich Nicolas Flammarion Martin Jaggi FedML 18 13 0 10 Nov 2021
Nearly Optimal Linear Convergence of Stochastic Primal-Dual Methods for Linear Programming Haihao Lu Jinwen Yang 14 6 0 10 Nov 2021
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning Raed Al Kontar Naichen Shi Xubo Yue Seokhyun Chung E. Byon ... C. Okwudire Garvesh Raskutti R. Saigal Karandeep Singh Ye Zhisheng FedML 38 50 0 09 Nov 2021
Fast Line Search for Multi-Task Learning A. Filatov D. Merkulov 17 0 0 02 Oct 2021
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou Anthony Man-Cho So James Cheng 19 1 0 30 Sep 2021
Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features Bruce W. Lee Yoonna Jang J. Lee VLM 41 75 0 25 Sep 2021
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information Majid Jahani S. Rusakov Zheng Shi Peter Richtárik Michael W. Mahoney Martin Takávc ODL 16 25 0 11 Sep 2021
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees Hamid Reza Feyzmahdavian M. Johansson 30 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 16 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 24 9 0 10 Aug 2021
Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control Xin-Yang Liu Jian-Xun Wang AI4CE 25 38 0 31 Jul 2021
Coordinate-wise Control Variates for Deep Policy Gradients Yuanyi Zhong Yuanshuo Zhou Jian-wei Peng BDL 14 1 0 11 Jul 2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity Nicolas Loizou Hugo Berard Gauthier Gidel Ioannis Mitliagkas Simon Lacoste-Julien 26 53 0 30 Jun 2021
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence Daogao Liu Zhou Lu LRM 16 1 0 28 Jun 2021
Stochastic Polyak Stepsize with a Moving Target Robert Mansel Gower Aaron Defazio Michael G. Rabbat 29 17 0 22 Jun 2021
Adaptive Learning Rate and Momentum for Training Deep Neural Networks Zhiyong Hao Yixuan Jiang Huihua Yu H. Chiang ODL 19 9 0 22 Jun 2021
Kernel Clustering with Sigmoid-based Regularization for Efficient Segmentation of Sequential Data Tung Doan Atsuhiro Takasu 35 1 0 22 Jun 2021
Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processing Carlos Basto 8 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 22 12 0 25 Apr 2021
Generalization of GANs and overparameterized models under Lipschitz continuity Khoat Than Nghia D. Vu AI4CE 22 2 0 06 Apr 2021
Stochastic Reweighted Gradient Descent Ayoub El Hanchi D. Stephens 16 8 0 23 Mar 2021
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Zhize Li 43 14 0 21 Mar 2021
Piecewise linear regression and classification Alberto Bemporad 14 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-Wen Zhang 16 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 32 13 0 19 Feb 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark W. Schmidt Simon Lacoste-Julien 18 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 46 67 0 17 Feb 2021
Distributed Second Order Methods with Fast Rates and Compressed Communication Rustem Islamov Xun Qian Peter Richtárik 32 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 92 946 0 03 Feb 2021
Variational Neural Annealing Mohamed Hibat-Allah E. Inack R. Wiersema R. Melko Juan Carrasquilla DRL 11 80 0 25 Jan 2021
Minibatch optimal transport distances; analysis and applications Kilian Fatras Younes Zine Szymon Majewski Rémi Flamary Rémi Gribonval Nicolas Courty OT 71 53 0 05 Jan 2021
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications Xiang Li Zhihua Zhang 24 4 0 05 Jan 2021
Learning Sign-Constrained Support Vector Machines Kenya Tajima Takahiko Henmi Kohei Tsuchida E. R. R. Zara Tsuyoshi Kato 9 1 0 05 Jan 2021
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization Jinshan Zeng Yixuan Zha Ke Ma Yuan Yao 9 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 13 9 0 29 Dec 2020
Stochastic Gradient Variance Reduction by Solving a Filtering Problem Xingyi Yang 11 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
Recent Theoretical Advances in Non-Convex Optimization Marina Danilova Pavel Dvurechensky Alexander Gasnikov Eduard A. Gorbunov Sergey Guminov Dmitry Kamzolov Innokentiy Shibaev 31 76 0 11 Dec 2020
Optimising cost vs accuracy of decentralised analytics in fog computing environments Lorenzo Valerio A. Passarella M. Conti 32 1 0 09 Dec 2020
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 19 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 8 0 0 01 Dec 2020
Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration Michael B. Cohen Aaron Sidford Kevin Tian 13 39 0 12 Nov 2020
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering Ricky T. Q. Chen Dami Choi Lukas Balles David Duvenaud Philipp Hennig ODL 44 6 0 09 Nov 2020