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
A Flexible Fairness Framework with Surrogate Loss Reweighting for Addressing Sociodemographic Disparities Wen Xu Elham Dolatabadi FaML 89 0 0 21 Mar 2025
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration Xue Feng M. Paul Laiu Thomas Strohmer FedML 65 0 0 14 Mar 2025
Adaptive Extrapolated Proximal Gradient Methods with Variance Reduction for Composite Nonconvex Finite-Sum Minimization Ganzhao Yuan 43 0 0 28 Feb 2025
Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems Matteo Lapucci Davide Pucci ODL 27 0 0 11 Nov 2024
Analysis of ELSA COVID-19 Substudy response rate using machine learning algorithms Marjan Qazvini 25 0 0 01 Nov 2024
Sample-Efficient Agnostic Boosting Udaya Ghai Karan Singh 27 0 0 31 Oct 2024
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration Qinglun Li Miao Zhang Yingqi Liu Quanjun Yin Li Shen Xiaochun Cao FedML 41 0 0 09 Oct 2024
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement Qinglun Li Miao Zhang Mengzhu Wang Quanjun Yin Li Shen OODD FedML 26 0 0 09 Oct 2024
FedScalar: A Communication efficient Federated Learning M. Rostami S. S. Kia FedML 20 1 0 03 Oct 2024
On the SAGA algorithm with decreasing step Luis Fredes Bernard Bercu Eméric Gbaguidi 21 1 0 02 Oct 2024
Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models Matteo Lapucci Davide Pucci 35 1 0 06 Aug 2024
Sequential Gaussian Variational Inference for Nonlinear State Estimation applied to Robotic Applications Min-Won Seo Solmaz S. Kia 43 0 0 07 Jul 2024
CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System Ge Fan Chaoyun Zhang Kai Wang Yingjie Li Junyang Chen Zenglin Xu 36 1 0 28 Jun 2024
Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction E. Papoutsellis Casper O. da Costa-Luis D. Deidda C. Delplancke Margaret Duff ... Ž. Kereta E. Ovtchinnikov Edoardo Pasca Georg Schramm Kris Thielemans 24 0 0 21 Jun 2024
Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data Xiaolu Wang Yuchang Sun Hoi-To Wai Jun Zhang 49 0 0 27 May 2024
FedStale: leveraging stale client updates in federated learning Angelo Rodio Giovanni Neglia FedML 35 4 0 07 May 2024
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients Sachin Garg A. Berahas Michal Dereziñski 38 1 0 23 Apr 2024
Stochastic Online Optimization for Cyber-Physical and Robotic Systems Hao Ma M. Zeilinger Michael Muehlebach 44 0 0 08 Apr 2024
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction Yury Demidovich Grigory Malinovsky Peter Richtárik 56 2 0 11 Mar 2024
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition Yunyan Bai Yuxing Liu Luo Luo 26 0 0 04 Feb 2024
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates Zhuanghua Liu Luo Luo K. H. Low 11 2 0 04 Feb 2024
Continuous-time Riemannian SGD and SVRG Flows on Wasserstein Probabilistic Space Mingyang Yi Bohan Wang 26 0 0 24 Jan 2024
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks Jie Hu Vishwaraj Doshi Do Young Eun 50 2 0 18 Jan 2024
Stochastic optimization with arbitrary recurrent data sampling William G. Powell Hanbaek Lyu 37 0 0 15 Jan 2024
Online estimation of the inverse of the Hessian for stochastic optimization with application to universal stochastic Newton algorithms Antoine Godichon-Baggioni Wei Lu Bruno Portier 36 1 0 15 Jan 2024
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners Rachel Redberg Antti Koskela Yu-Xiang Wang 68 5 0 31 Dec 2023
A Coefficient Makes SVRG Effective Yida Yin Zhiqiu Xu Zhiyuan Li Trevor Darrell Zhuang Liu 33 1 0 09 Nov 2023
$Computing Approximate $\ell_p$ Sensitivities$ Computing Approximate $\ell_p$ Sensitivities Swati Padmanabhan David P. Woodruff Qiuyi Zhang 45 0 0 07 Nov 2023
Signal Processing Meets SGD: From Momentum to Filter Zhipeng Yao Guisong Chang Jiaqi Zhang Qi Zhang Dazhou Li Yu Zhang ODL 29 0 0 06 Nov 2023
A Variational Perspective on High-Resolution ODEs Hoomaan Maskan K. C. Zygalakis A. Yurtsever 37 3 0 03 Nov 2023
Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models E. Sidrow Nancy Heckman Alexandre Bouchard-Coté S. Fortune A. Trites M. Auger-Méthé 13 1 0 06 Oct 2023
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions Xu Cai Ahmet Alacaoglu Jelena Diakonikolas 46 7 0 04 Oct 2023
On the Parallel Complexity of Multilevel Monte Carlo in Stochastic Gradient Descent Kei Ishikawa BDL 63 0 0 03 Oct 2023
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data Xiaolu Wang Cheng Jin Hoi-To Wai Yuantao Gu 14 4 0 10 Sep 2023
Variance reduction techniques for stochastic proximal point algorithms Cheik Traoré Vassilis Apidopoulos Saverio Salzo S. Villa 28 4 0 18 Aug 2023
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction Xiao-Yan Jiang Sebastian U. Stich 25 18 0 11 Aug 2023
Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions Le‐Yu Chen Boyuan Yao Luo Luo 19 15 0 29 Jul 2023
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? Kyurae Kim Yian Ma Jacob R. Gardner 26 5 0 27 Jul 2023
A hybrid machine learning framework for clad characteristics prediction in metal additive manufacturing S. Tayebati K. Cho AI4CE 13 3 0 04 Jul 2023
Efficient preconditioned stochastic gradient descent for estimation in latent variable models C. Baey Maud Delattre E. Kuhn Jean-Benoist Léger Sarah Lemler 21 4 0 22 Jun 2023
Sharpened Lazy Incremental Quasi-Newton Method Aakash Lahoti Spandan Senapati K. Rajawat Alec Koppel 29 2 0 26 May 2023
SignSVRG: fixing SignSGD via variance reduction Evgenii Chzhen S. Schechtman 22 2 0 22 May 2023
Online Learning Under A Separable Stochastic Approximation Framework Min Gan Xiang-Xiang Su Guang-yong Chen Jing Chen 28 0 0 12 May 2023
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features Aleksandr Beznosikov David Dobre Gauthier Gidel 25 5 0 23 Apr 2023
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization Zebang Shen Hui Qian Tongzhou Mu Chao Zhang ODL 9 0 0 23 Apr 2023
Debiasing Conditional Stochastic Optimization Lie He S. Kasiviswanathan CML BDL 46 4 0 20 Apr 2023
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints Pierre Ablin Simon Vary Bin Gao P.-A. Absil 49 7 0 29 Mar 2023
Unified analysis of SGD-type methods Eduard A. Gorbunov 27 2 0 29 Mar 2023
Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization Cheuk Yin Lin Chaobing Song Jelena Diakonikolas 19 5 0 28 Mar 2023
Convergence of variational Monte Carlo simulation and scale-invariant pre-training Nilin Abrahamsen Zhiyan Ding Gil Goldshlager Lin Lin DRL 30 2 0 21 Mar 2023