Exact and Inexact Subsampled Newton Methods for Optimization

27 September 2016

Papers citing "Exact and Inexact Subsampled Newton Methods for Optimization"

22 / 22 papers shown

Title
A stochastic gradient method for trilevel optimization Tommaso Giovannelli G. Kent Luis Nunes Vicente 34 0 0 11 May 2025
Efficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization Youran Dong Junfeng Yang Wei-Ting Yao Jin Zhang 135 0 0 04 May 2025
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Jingruo Sun Zachary Frangella Madeleine Udell 36 0 0 28 Jan 2025
An Efficient Nonlinear Acceleration method that Exploits Symmetry of the Hessian Huan He Shifan Zhao Z. Tang Joyce C. Ho Y. Saad Yuanzhe Xi 21 3 0 22 Oct 2022
SP2: A Second Order Stochastic Polyak Method Shuang Li W. Swartworth Martin Takávc Deanna Needell Robert Mansel Gower 23 13 0 17 Jul 2022
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches Michal Derezinski 52 5 0 06 Jun 2022
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence Sen Na Michal Derezinski Michael W. Mahoney 27 16 0 20 Apr 2022
Large-Scale Deep Learning Optimizations: A Comprehensive Survey Xiaoxin He Fuzhao Xue Xiaozhe Ren Yang You 27 14 0 01 Nov 2021
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks Elizabeth Newman Julianne Chung Matthias Chung Lars Ruthotto 47 6 0 28 Sep 2021
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization Raghu Bollapragada Stefan M. Wild 29 11 0 24 Sep 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark W. Schmidt Simon Lacoste-Julien 18 18 0 18 Feb 2021
Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds Jonathan Lacotte Mert Pilanci 20 11 0 13 Dec 2020
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training Diego Granziol S. Zohren Stephen J. Roberts ODL 37 48 0 16 Jun 2020
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm Majid Jahani M. Nazari R. Tappenden A. Berahas Martin Takávc ODL 11 10 0 06 Jun 2020
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning Z. Yao A. Gholami Sheng Shen Mustafa Mustafa Kurt Keutzer Michael W. Mahoney ODL 13 273 0 01 Jun 2020
Adversarial Classification via Distributional Robustness with Wasserstein Ambiguity Nam Ho-Nguyen Stephen J. Wright OOD 42 16 0 28 May 2020
Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex Optimization Thomas O'Leary-Roseberry Nick Alger Omar Ghattas ODL 37 9 0 07 Feb 2020
High-Dimensional Optimization in Adaptive Random Subspaces Jonathan Lacotte Mert Pilanci Marco Pavone 27 16 0 27 Jun 2019
Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample A. Berahas Majid Jahani Peter Richtárik Martin Takávc 21 40 0 28 Jan 2019
GPU Accelerated Sub-Sampled Newton's Method Sudhir B. Kylasa Farbod Roosta-Khorasani Michael W. Mahoney A. Grama ODL 20 8 0 26 Feb 2018
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning Frank E. Curtis K. Scheinberg 33 45 0 30 Jun 2017
Generalized Self-Concordant Functions: A Recipe for Newton-Type Methods Tianxiao Sun Quoc Tran-Dinh 16 60 0 14 Mar 2017