Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory

4 June 2017

Papers citing "Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory"

17 / 17 papers shown

Title
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality Kejie Tang Weidong Liu Yichen Zhang Xi Chen 26 2 0 28 May 2023
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions S. Choudhury Eduard A. Gorbunov Nicolas Loizou 34 13 0 27 Feb 2023
Distributed Stochastic Optimization under a General Variance Condition Kun-Yen Huang Xiao Li Shin-Yi Pu FedML 45 6 0 30 Jan 2023
ALS: Augmented Lagrangian Sketching Methods for Linear Systems M. Morshed 36 0 0 12 Aug 2022
Towards Practical Large-scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification Nathaniel Pritchard V. Patel 26 1 0 09 Aug 2022
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation Rustem Islamov Xun Qian Slavomír Hanzely M. Safaryan Peter Richtárik 45 16 0 07 Jun 2022
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods Aleksandr Beznosikov Eduard A. Gorbunov Hugo Berard Nicolas Loizou 24 49 0 15 Feb 2022
Solving, Tracking and Stopping Streaming Linear Inverse Problems Nathaniel Pritchard V. Patel 26 0 0 15 Jan 2022
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
Stochastic Extragradient: General Analysis and Improved Rates Eduard A. Gorbunov Hugo Berard Gauthier Gidel Nicolas Loizou 22 40 0 16 Nov 2021
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 42 0 0 26 Aug 2020
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation Robert Mansel Gower Othmane Sebbouh Nicolas Loizou 30 74 0 18 Jun 2020
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence Nicolas Loizou Sharan Vaswani I. Laradji Simon Lacoste-Julien 29 181 0 24 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 17 180 0 09 Feb 2020
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates Adil Salim D. Kovalev Peter Richtárik 27 25 0 28 May 2019
SEGA: Variance Reduction via Gradient Sketching Filip Hanzely Konstantin Mishchenko Peter Richtárik 25 71 0 09 Sep 2018
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 24 200 0 27 Dec 2017