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Adaptive Newton Sketch: Linear-time Optimization with Quadratic
  Convergence and Effective Hessian Dimensionality

Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality

15 May 2021
Jonathan Lacotte
Yifei Wang
Mert Pilanci
ArXivPDFHTML

Papers citing "Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality"

12 / 12 papers shown
Title
FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch
FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch
Sunny Gupta
Mohit Jindal
Pankhi Kashyap
Pranav Jeevan
Amit Sethi
FedML
34
0
0
23 Sep 2024
Cubic regularized subspace Newton for non-convex optimization
Cubic regularized subspace Newton for non-convex optimization
Jim Zhao
Aurelien Lucchi
N. Doikov
33
5
0
24 Jun 2024
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
Jian Li
Yong Liu
Wei Wang
Haoran Wu
Weiping Wang
FedML
36
2
0
05 Jan 2024
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic
  Superlinear Convergence Rate
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate
Zhan Gao
Aryan Mokhtari
Alec Koppel
23
2
0
27 Jun 2023
Constrained Optimization via Exact Augmented Lagrangian and Randomized
  Iterative Sketching
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
Ilgee Hong
Sen Na
Michael W. Mahoney
Mladen Kolar
33
3
0
28 May 2023
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature
  Estimates
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates
Zachary Frangella
Pratik Rathore
Shipu Zhao
Madeleine Udell
18
5
0
16 Nov 2022
DRSOM: A Dimension Reduced Second-Order Method
DRSOM: A Dimension Reduced Second-Order Method
Chuwen Zhang
Dongdong Ge
Chang He
Bo Jiang
Yuntian Jiang
Yi-Li Ye
21
4
0
30 Jul 2022
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear
  Convergence
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence
Sen Na
Michal Derezinski
Michael W. Mahoney
27
16
0
20 Apr 2022
Nys-Newton: Nyström-Approximated Curvature for Stochastic Optimization
Nys-Newton: Nyström-Approximated Curvature for Stochastic Optimization
Dinesh Singh
Hardik Tankaria
M. Yamada
ODL
47
2
0
16 Oct 2021
Analytic Insights into Structure and Rank of Neural Network Hessian Maps
Analytic Insights into Structure and Rank of Neural Network Hessian Maps
Sidak Pal Singh
Gregor Bachmann
Thomas Hofmann
FAtt
28
34
0
30 Jun 2021
Distributed Sketching Methods for Privacy Preserving Regression
Distributed Sketching Methods for Privacy Preserving Regression
Burak Bartan
Mert Pilanci
29
11
0
16 Feb 2020
Sharp analysis of low-rank kernel matrix approximations
Sharp analysis of low-rank kernel matrix approximations
Francis R. Bach
86
281
0
09 Aug 2012
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