Papers citing 'Distributed estimation of the inverse Hessian by determinantal averaging'

Title
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization Mrinmay Sen C Krishna Mohan 159 0 0 08 Jun 2025
Accelerated Training of Federated Learning via Second-Order Methods Mrinmay Sen Sidhant R Nair C Krishna Mohan FedML 195 0 0 29 May 2025
Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and DebiasingInternational Conference on Learning Representations (ICLR), 2024 Elad Romanov Fangzhao Zhang Mert Pilanci 142 1 0 02 Oct 2024
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning Michał Dereziński Michael W. Mahoney 240 18 0 17 Jun 2024
Stochastic Newton Proximal Extragradient Method Ruichen Jiang Michal Dereziñski Aryan Mokhtari 92 0 0 03 Jun 2024
Distributed Least Squares in Small Space via Sketching and Bias ReductionNeural Information Processing Systems (NeurIPS), 2024 Sachin Garg Kevin Tan Michal Dereziñski 162 2 0 08 May 2024
FAGH: Accelerating Federated Learning with Approximated Global Hessian Mrinmay Sen A. K. Qin Krishna Mohan FedML 211 1 0 16 Mar 2024
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm Xinteng Ma Renyi Bao Jinpeng Jiang Yang Liu Arthur Jiang Junhua Yan Xin Liu Zhisong Pan FedML 227 8 0 20 Jun 2022
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches Michal Derezinski 311 11 0 06 Jun 2022
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear ConvergenceMathematical programming (Math. Program.), 2022 Sen Na Michal Derezinski Michael W. Mahoney 288 21 0 20 Apr 2022
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing Nicolò Dal Fabbro S. Dey M. Rossi Luca Schenato FedML 317 17 0 11 Feb 2022
Learning Linear Models Using Distributed Iterative Hessian Sketching Han Wang James Anderson 203 2 0 08 Dec 2021
On Second-order Optimization Methods for Federated Learning Sebastian Bischoff Stephan Günnemann Martin Jaggi Sebastian U. Stich FedML 127 12 0 06 Sep 2021
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton UpdateNeural Information Processing Systems (NeurIPS), 2021 Michal Derezinski Jonathan Lacotte Mert Pilanci Michael W. Mahoney 282 30 0 15 Jul 2021
LocalNewton: Reducing Communication Bottleneck for Distributed Learning Vipul Gupta Avishek Ghosh Michal Derezinski Rajiv Khanna Kannan Ramchandran Michael W. Mahoney 168 13 0 16 May 2021
Sparse sketches with small inversion biasAnnual Conference Computational Learning Theory (COLT), 2020 Michal Derezinski Zhenyu Liao Guang Cheng Michael W. Mahoney 410 23 0 21 Nov 2020
Operator Shifting for Noisy Elliptic SystemsResearch in the Mathematical Sciences (Res. Math. Sci.), 2020 Philip A. Etter Lexing Ying 359 2 0 19 Oct 2020
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization Michal Derezinski Burak Bartan Mert Pilanci Michael W. Mahoney 151 27 0 02 Jul 2020
Precise expressions for random projections: Low-rank approximation and randomized Newton Michal Derezinski Feynman T. Liang Zhenyu A. Liao Michael W. Mahoney 364 24 0 18 Jun 2020
Distributed Newton Can Communicate Less and Resist Byzantine Workers Avishek Ghosh R. Maity A. Mazumdar FedML 160 35 0 15 Jun 2020
Determinantal Point Processes in Randomized Numerical Linear Algebra Michal Derezinski Michael W. Mahoney 194 91 0 07 May 2020
Exact expressions for double descent and implicit regularization via surrogate random designNeural Information Processing Systems (NeurIPS), 2019 Michal Derezinski Feynman T. Liang Michael W. Mahoney 277 79 0 10 Dec 2019
Bayesian experimental design using regularized determinantal point processesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019 Michal Derezinski Feynman T. Liang Michael W. Mahoney 113 27 0 10 Jun 2019