Stochastic Dual Coordinate Ascent with Adaptive Probabilities

27 February 2015

Papers citing "Stochastic Dual Coordinate Ascent with Adaptive Probabilities"

43 / 43 papers shown

Title
Towards a Better Theoretical Understanding of Independent Subnetwork Training Egor Shulgin Peter Richtárik AI4CE 4 6 0 28 Jun 2023
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models Ethan Pickering T. Sapsis 8 5 0 27 Aug 2022
Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems Yunwen Lei 9 14 0 14 Jun 2022
SGD with Coordinate Sampling: Theory and Practice Rémi Leluc Franccois Portier 9 2 0 25 May 2021
Adam with Bandit Sampling for Deep Learning Rui Liu Tianyi Wu Barzan Mozafari 4 22 0 24 Oct 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark W. Schmidt Francis R. Bach Peter Richtárik 6 109 0 02 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 11 0 0 26 Aug 2020
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks Weilin Cong R. Forsati M. Kandemir M. Mahdavi 8 76 0 24 Jun 2020
Stochastic batch size for adaptive regularization in deep network optimization Kensuke Nakamura Stefano Soatto Byung-Woo Hong ODL 6 5 0 14 Apr 2020
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization Sudeep Salgia Qing Zhao Sattar Vakili 24 2 0 11 Mar 2020
Straggler-Agnostic and Communication-Efficient Distributed Primal-Dual Algorithm for High-Dimensional Data Mining Zhouyuan Huo Heng-Chiao Huang FedML 7 4 0 09 Oct 2019
Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip Nicolas Loizou 11 5 0 26 Sep 2019
Nearly Consistent Finite Particle Estimates in Streaming Importance Sampling Alec Koppel Amrit Singh Bedi Brian M. Sadler Victor Elvira 6 2 0 23 Sep 2019
ADASS: Adaptive Sample Selection for Training Acceleration Shen-Yi Zhao Hao Gao Wu-Jun Li 11 0 0 11 Jun 2019
On Linear Learning with Manycore Processors Eliza Wszola Celestine Mendler-Dünner Martin Jaggi Markus Püschel 11 1 0 02 May 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 16 44 0 25 Jan 2019
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop D. Kovalev Samuel Horváth Peter Richtárik 23 154 0 24 Jan 2019
Double Adaptive Stochastic Gradient Optimization Rajaditya Mukherjee Jin Li Shicheng Chu Huamin Wang ODL 14 0 0 06 Nov 2018
Accelerating Stochastic Gradient Descent Using Antithetic Sampling Jingchang Liu Linli Xu 6 2 0 07 Oct 2018
A Fast, Principled Working Set Algorithm for Exploiting Piecewise Linear Structure in Convex Problems Tyler B. Johnson Carlos Guestrin 6 5 0 20 Jul 2018
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields Rémi Le Priol Alexandre Piché Simon Lacoste-Julien 14 5 0 22 Dec 2017
Coordinate Descent with Bandit Sampling Farnood Salehi Patrick Thiran L. E. Celis 16 17 0 08 Dec 2017
Safe Adaptive Importance Sampling Sebastian U. Stich Anant Raj Martin Jaggi 16 52 0 07 Nov 2017
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems Celestine Mendler-Dünner Thomas Parnell Martin Jaggi FedML 26 0 0 17 Aug 2017
Stochastic, Distributed and Federated Optimization for Machine Learning Jakub Konecný FedML 13 38 0 04 Jul 2017
Approximate Steepest Coordinate Descent Sebastian U. Stich Anant Raj Martin Jaggi 6 15 0 26 Jun 2017
IS-ASGD: Accelerating Asynchronous SGD using Importance Sampling Fei Wang Jun Ye Weichen Li Guihai Chen 17 1 0 26 Jun 2017
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications A. Chambolle Matthias Joachim Ehrhardt Peter Richtárik Carola-Bibiane Schönlieb 14 182 0 15 Jun 2017
Stochastic Primal Dual Coordinate Method with Non-Uniform Sampling Based on Optimality Violations Atsushi Shibagaki Ichiro Takeuchi 16 5 0 21 Mar 2017
Faster Coordinate Descent via Adaptive Importance Sampling Dmytro Perekrestenko V. Cevher Martin Jaggi 6 42 0 07 Mar 2017
Linear convergence of SDCA in statistical estimation C. Qu Huan Xu 33 8 0 26 Jan 2017
A Primer on Coordinate Descent Algorithms Hao-Jun Michael Shi Shenyinying Tu Yangyang Xu W. Yin 24 91 0 30 Sep 2016
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs A. Osokin Jean-Baptiste Alayrac Isabella Lukasewitz P. Dokania Simon Lacoste-Julien 17 64 0 30 May 2016
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing Chenxin Ma Martin Takáč 23 10 0 16 Mar 2016
Importance Sampling for Minibatches Dominik Csiba Peter Richtárik 14 113 0 06 Feb 2016
Reducing Runtime by Recycling Samples Jialei Wang Hai Wang Nathan Srebro 24 3 0 05 Feb 2016
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling Zeyuan Allen-Zhu Zheng Qu Peter Richtárik Yang Yuan 28 172 0 30 Dec 2015
Distributed Optimization with Arbitrary Local Solvers Chenxin Ma Jakub Konecný Martin Jaggi Virginia Smith Michael I. Jordan Peter Richtárik Martin Takáč 19 182 0 13 Dec 2015
Dual Free Adaptive Mini-batch SDCA for Empirical Risk Minimization Xi He Martin Takávc 10 1 0 22 Oct 2015
Doubly Stochastic Primal-Dual Coordinate Method for Bilinear Saddle-Point Problem Adams Wei Yu Qihang Lin Tianbao Yang 20 7 0 14 Aug 2015
Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex Losses Dominik Csiba Peter Richtárik 17 22 0 07 Jun 2015
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 76 736 0 19 Mar 2014
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 52 317 0 18 Feb 2014