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Accelerating Stochastic Gradient Descent For Least Squares Regression

26 April 2017

Papers citing "Accelerating Stochastic Gradient Descent For Least Squares Regression"

28 / 28 papers shown

Title
Distributionally Robust Learning with Weakly Convex Losses: Convergence Rates and Finite-Sample Guarantees Landi Zhu Mert Gurbuzbalaban A. Ruszczynski 78 7 0 16 Jan 2023
Flatter, faster: scaling momentum for optimal speedup of SGD Aditya Cowsik T. Can Paolo Glorioso 98 5 0 28 Oct 2022
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo Sébastien M. R. Arnold P. LÉcuyer Liyu Chen Yi-fan Chen Fei Sha OffRL 82 4 0 16 Feb 2022
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness Sarah Sachs Hédi Hadiji T. Erven Cristóbal Guzmán 141 17 0 15 Feb 2022
COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization Manuel Madeira Renato M. P. Negrinho J. Xavier P. Aguiar 37 0 0 07 Sep 2021
Anytime Minibatch with Delayed Gradients H. Al-Lawati S. Draper 59 0 0 15 Dec 2020
Federated Composite Optimization Honglin Yuan Manzil Zaheer Sashank J. Reddi FedML 87 61 0 17 Nov 2020
The Heavy-Tail Phenomenon in SGD Mert Gurbuzbalaban Umut Simsekli Lingjiong Zhu 59 130 0 08 Jun 2020
Optimization for deep learning: theory and algorithms Ruoyu Sun ODL 137 169 0 19 Dec 2019
The Wang-Landau Algorithm as Stochastic Optimization and Its Acceleration Chenguang Dai Jun S. Liu 60 4 0 27 Jul 2019
The Role of Memory in Stochastic Optimization Antonio Orvieto Jonas Köhler Aurelien Lucchi 94 31 0 02 Jul 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 84 28 0 08 Jun 2019
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares Rong Ge Sham Kakade Rahul Kidambi Praneeth Netrapalli 125 155 0 29 Apr 2019
Communication trade-offs for synchronized distributed SGD with large step size Kumar Kshitij Patel Aymeric Dieuleveut FedML 66 27 0 25 Apr 2019
Memory-Sample Tradeoffs for Linear Regression with Small Error Vatsal Sharan Aaron Sidford Gregory Valiant 77 35 0 18 Apr 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 206 135 0 10 Apr 2019
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions Adrien B. Taylor Francis R. Bach 79 64 0 03 Feb 2019
A Universally Optimal Multistage Accelerated Stochastic Gradient Method N. Aybat Alireza Fallah Mert Gurbuzbalaban Asuman Ozdaglar ODL 114 57 0 23 Jan 2019
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances Bugra Can Mert Gurbuzbalaban Lingjiong Zhu 102 45 0 22 Jan 2019
Quasi-hyperbolic momentum and Adam for deep learning Jerry Ma Denis Yarats ODL 159 130 0 16 Oct 2018
Optimal Adaptive and Accelerated Stochastic Gradient Descent Qi Deng Yi Cheng Guanghui Lan 58 8 0 01 Oct 2018
Optimal Matrix Momentum Stochastic Approximation and Applications to Q-learning Adithya M. Devraj Ana Bušić Sean P. Meyn 128 4 0 17 Sep 2018
On the insufficiency of existing momentum schemes for Stochastic Optimization Rahul Kidambi Praneeth Netrapalli Prateek Jain Sham Kakade ODL 98 120 0 15 Mar 2018
Accelerated Gradient Boosting Gérard Biau B. Cadre L. Rouviere 84 113 0 06 Mar 2018
Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains Aymeric Dieuleveut Alain Durmus Francis R. Bach 108 156 0 20 Jul 2017
Accelerated Stochastic Power Iteration Christopher De Sa Bryan D. He Ioannis Mitliagkas Christopher Ré Peng Xu 88 91 0 10 Jul 2017
Stochastic Heavy Ball S. Gadat Fabien Panloup Sofiane Saadane 122 105 0 14 Sep 2016
Fast Incremental Method for Nonconvex Optimization Sashank J. Reddi S. Sra Barnabás Póczós Alex Smola 95 44 0 19 Mar 2016