Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information

23 August 2017

Papers citing "Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information"

33 / 33 papers shown

Title
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Jingruo Sun Zachary Frangella Madeleine Udell 36 0 0 28 Jan 2025
Cubic regularized subspace Newton for non-convex optimization Jim Zhao Aurelien Lucchi N. Doikov 30 5 0 24 Jun 2024
On Newton's Method to Unlearn Neural Networks Nhung Bui Xinyang Lu Rachael Hwee Ling Sim See-Kiong Ng Bryan Kian Hsiang Low MU 43 2 0 20 Jun 2024
Level Set Teleportation: An Optimization Perspective Aaron Mishkin A. Bietti Robert Mansel Gower 36 1 0 05 Mar 2024
Second-Order Fine-Tuning without Pain for LLMs:A Hessian Informed Zeroth-Order Optimizer Yanjun Zhao Sizhe Dang Haishan Ye Guang Dai Yi Qian Ivor W.Tsang 66 8 0 23 Feb 2024
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods El Mahdi Chayti N. Doikov Martin Jaggi ODL 27 5 0 23 Feb 2023
Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularization Yian Deng Tingting Mu 21 1 0 22 Feb 2023
Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee Tianyi Lin P. Mertikopoulos Michael I. Jordan 26 11 0 23 Oct 2022
Augmented Newton Method for Optimization: Global Linear Rate and Momentum Interpretation M. Morshed ODL 24 1 0 23 May 2022
Efficient Convex Optimization Requires Superlinear Memory A. Marsden Vatsal Sharan Aaron Sidford Gregory Valiant 29 14 0 29 Mar 2022
Tackling benign nonconvexity with smoothing and stochastic gradients Harsh Vardhan Sebastian U. Stich 28 8 0 18 Feb 2022
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization Raghu Bollapragada Stefan M. Wild 35 11 0 24 Sep 2021
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update Michal Derezinski Jonathan Lacotte Mert Pilanci Michael W. Mahoney 40 26 0 15 Jul 2021
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 34 0 0 26 Aug 2020
Precise expressions for random projections: Low-rank approximation and randomized Newton Michal Derezinski Feynman T. Liang Zhenyu A. Liao Michael W. Mahoney 34 23 0 18 Jun 2020
A block coordinate descent optimizer for classification problems exploiting convexity Ravi G. Patel N. Trask Mamikon A. Gulian E. Cyr ODL 30 7 0 17 Jun 2020
Practical Quasi-Newton Methods for Training Deep Neural Networks D. Goldfarb Yi Ren Achraf Bahamou ODL 8 104 0 16 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 36 273 0 01 Jun 2020
Adaptive Stochastic Optimization Frank E. Curtis K. Scheinberg ODL 16 29 0 18 Jan 2020
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies Kaipeng Zhang Alec Koppel Haoqi Zhu Tamer Basar 44 186 0 19 Jun 2019
Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample A. Berahas Majid Jahani Peter Richtárik Martin Takávc 24 40 0 28 Jan 2019
A note on solving nonlinear optimization problems in variable precision Serge Gratton P. Toint 22 13 0 09 Dec 2018
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property Yi Zhou Zhe Wang Yingbin Liang 24 23 0 22 Aug 2018
Stochastic Nested Variance Reduction for Nonconvex Optimization Dongruo Zhou Pan Xu Quanquan Gu 25 146 0 20 Jun 2018
Local Saddle Point Optimization: A Curvature Exploitation Approach Leonard Adolphs Hadi Daneshmand Aurelien Lucchi Thomas Hofmann 37 107 0 15 May 2018
Escaping Saddles with Stochastic Gradients Hadi Daneshmand Jonas Köhler Aurelien Lucchi Thomas Hofmann 24 161 0 15 Mar 2018
GPU Accelerated Sub-Sampled Newton's Method Sudhir B. Kylasa Farbod Roosta-Khorasani Michael W. Mahoney A. Grama ODL 26 8 0 26 Feb 2018
Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization Zhe Wang Yi Zhou Yingbin Liang Guanghui Lan 35 46 0 20 Feb 2018
NEON+: Accelerated Gradient Methods for Extracting Negative Curvature for Non-Convex Optimization Yi Tian Xu R. L. Jin Tianbao Yang 35 25 0 04 Dec 2017
On Noisy Negative Curvature Descent: Competing with Gradient Descent for Faster Non-convex Optimization Mingrui Liu Tianbao Yang 30 23 0 25 Sep 2017
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization Shusen Wang Farbod Roosta-Khorasani Peng Xu Michael W. Mahoney 36 127 0 11 Sep 2017
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 183 1,185 0 30 Nov 2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 93 737 0 19 Mar 2014