Stochastic Newton and Cubic Newton Methods with Simple Local
Linear-Quadratic Rates

Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates

3 December 2019

Konstantin Mishchenko

Peter Richtárik

Papers citing "Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates"

16 / 16 papers shown

Title
A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning Zhehao Huang Xinwen Cheng Jie Zhang Jinghao Zheng Haoran Wang Zhengbao He Tao Li Xiaolin Huang CLL 35 0 0 21 May 2025
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication Kumar Kshitij Patel Margalit Glasgow Ali Zindari Lingxiao Wang Sebastian U. Stich Ziheng Cheng Nirmit Joshi Nathan Srebro 48 6 0 19 May 2024
Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods Kimon Antonakopoulos Ali Kavis V. Cevher ODL 34 12 0 03 Nov 2022
Super-Universal Regularized Newton Method N. Doikov Konstantin Mishchenko Y. Nesterov 14 29 0 11 Aug 2022
SP2: A Second Order Stochastic Polyak Method Shuang Li W. Swartworth Martin Takávc Deanna Needell Robert Mansel Gower 29 13 0 17 Jul 2022
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation Rustem Islamov Xun Qian Slavomír Hanzely M. Safaryan Peter Richtárik 40 16 0 07 Jun 2022
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches Michal Derezinski 55 6 0 06 Jun 2022
Augmented Newton Method for Optimization: Global Linear Rate and Momentum Interpretation M. Morshed ODL 24 1 0 23 May 2022
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence Sen Na Michal Derezinski Michael W. Mahoney 27 16 0 20 Apr 2022
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning Xun Qian Rustem Islamov M. Safaryan Peter Richtárik FedML 24 23 0 02 Nov 2021
Distributed Second Order Methods with Fast Rates and Compressed Communication Rustem Islamov Xun Qian Peter Richtárik 34 51 0 14 Feb 2021
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark Schmidt Francis R. Bach Peter Richtárik 21 111 0 02 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 39 0 0 26 Aug 2020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization Samuel Horváth Lihua Lei Peter Richtárik Michael I. Jordan 57 30 0 13 Feb 2020
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate Aryan Mokhtari Mert Gurbuzbalaban Alejandro Ribeiro 35 36 0 01 Nov 2016
A Proximal Stochastic Quasi-Newton Algorithm Luo Luo Zihao Chen Zhihua Zhang Wu-Jun Li ODL 19 6 0 31 Jan 2016