Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
1607.01231
Cited By
v1
v2
v3
v4 (latest)
Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization
SIAM Journal on Optimization (SIAM J. Optim.), 2016
5 July 2016
Tianlin Li
Shiqian Ma
Shiqian Ma
Wen Liu
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization"
50 / 67 papers shown
Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization
Aditya Ranganath
Mukesh Singhal
Roummel Marcia
ODL
232
1
0
17 Feb 2025
Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming
Xiaoran Cheng
Sen Na
PINN
311
1
0
16 Sep 2024
A Stochastic Quasi-Newton Method for Non-convex Optimization with Non-uniform Smoothness
IEEE Conference on Decision and Control (CDC), 2024
Zhenyu Sun
Ermin Wei
364
1
0
22 Mar 2024
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates
Zhuanghua Liu
Luo Luo
K. H. Low
385
3
0
04 Feb 2024
A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization
Junwen Qiu
Xiao Li
Andre Milzarek
696
7
0
02 Dec 2023
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization
Siddharth Singh
Zack Sating
A. Bhatele
ODL
236
1
0
18 Oct 2023
Provably Convergent Plug-and-Play Quasi-Newton Methods
SIAM Journal of Imaging Sciences (JSIS), 2023
Hongwei Tan
Subhadip Mukherjee
Junqi Tang
Carola-Bibiane Schönlieb
450
29
0
09 Mar 2023
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
IEEE Transactions on Signal Processing (IEEE TSP), 2023
Hadrien Hendrikx
G. Fort
Eric Moulines
Hoi-To Wai
347
23
0
22 Feb 2023
FOSI: Hybrid First and Second Order Optimization
International Conference on Learning Representations (ICLR), 2023
Hadar Sivan
Moshe Gabel
Assaf Schuster
ODL
437
2
0
16 Feb 2023
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods
Neural Information Processing Systems (NeurIPS), 2022
Yanli Liu
Jianchao Tan
Tamer Basar
W. Yin
514
128
0
15 Nov 2022
Convergence Rates of Stochastic Zeroth-order Gradient Descent for Ł ojasiewicz Functions
Tianyu Wang
Yasong Feng
424
2
0
31 Oct 2022
SP2: A Second Order Stochastic Polyak Method
International Conference on Learning Representations (ICLR), 2022
Shuang Li
W. Swartworth
Martin Takávc
Deanna Needell
Robert Mansel Gower
229
17
0
17 Jul 2022
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
304
8
0
20 Jun 2022
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches
Michal Derezinski
443
11
0
06 Jun 2022
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning
M. Yousefi
Angeles Martinez
ODL
226
1
0
18 May 2022
Can We Do Better Than Random Start? The Power of Data Outsourcing
Yi Chen
Jing-rong Dong
Xin T. Tong
169
0
0
17 May 2022
A Novel Fast Exact Subproblem Solver for Stochastic Quasi-Newton Cubic Regularized Optimization
Jarad Forristal
J. Griffin
Wenwen Zhou
S. Yektamaram
ODL
241
0
0
19 Apr 2022
Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part I
IEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2022
Jiaojiao Zhang
Huikang Liu
Anthony Man-Cho So
Qing Ling
283
20
0
19 Jan 2022
Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size
Adityanarayanan Radhakrishnan
M. Belkin
Caroline Uhler
ODL
123
0
0
30 Dec 2021
Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning
Sanae Lotfi
Tiphaine Bonniot de Ruisselet
D. Orban
Andrea Lodi
ODL
211
1
0
29 Nov 2021
Bolstering Stochastic Gradient Descent with Model Building
TOP - An Official Journal of the Spanish Society of Statistics and Operations Research (TOP), 2021
Ş. Birbil
Özgür Martin
Gönenç Onay
Figen Oztoprak
ODL
420
1
0
13 Nov 2021
A theoretical and empirical study of new adaptive algorithms with additional momentum steps and shifted updates for stochastic non-convex optimization
C. Alecsa
265
1
0
16 Oct 2021
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization
Fu Wei
Chenglong Bao
Yang Liu
258
25
0
04 Oct 2021
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks
Elizabeth Newman
Julianne Chung
Matthias Chung
Lars Ruthotto
360
8
0
28 Sep 2021
Revisiting Recursive Least Squares for Training Deep Neural Networks
Chunyuan Zhang
Qi Song
Hui Zhou
Yi-gui Ou
Hongyao Deng
Laurence Yang
ODL
300
7
0
07 Sep 2021
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients
Feihu Huang
Junyi Li
Heng-Chiao Huang
ODL
720
52
0
15 Jun 2021
Tensor Normal Training for Deep Learning Models
Neural Information Processing Systems (NeurIPS), 2021
Yi Ren
Shiqian Ma
390
32
0
05 Jun 2021
Differentially private inference via noisy optimization
Annals of Statistics (Ann. Stat.), 2021
Marco Avella-Medina
Casey Bradshaw
Po-Ling Loh
FedML
565
37
0
19 Mar 2021
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
AAAI Conference on Artificial Intelligence (AAAI), 2021
Qingsong Zhang
Bin Gu
Cheng Deng
Heng-Chiao Huang
FedML
147
80
0
01 Mar 2021
Kronecker-factored Quasi-Newton Methods for Deep Learning
Yi Ren
Achraf Bahamou
Shiqian Ma
ODL
324
6
0
12 Feb 2021
An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians
Mathematical programming (Math. Program.), 2021
Sen Na
M. Anitescu
Mladen Kolar
355
58
0
10 Feb 2021
Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation
Sanae Lotfi
Tiphaine Bonniot de Ruisselet
D. Orban
Andrea Lodi
ODL
154
7
0
10 Dec 2020
Asynchronous Parallel Stochastic Quasi-Newton Methods
Parallel Computing (PC), 2020
Qianqian Tong
Guannan Liang
Xingyu Cai
Chunjiang Zhu
J. Bi
ODL
308
10
0
02 Nov 2020
An adaptive Hessian approximated stochastic gradient MCMC method
Journal of Computational Physics (JCP), 2020
Yating Wang
Wei Deng
Guang Lin
BDL
208
5
0
03 Oct 2020
Variance-Reduced Methods for Machine Learning
Proceedings of the IEEE (Proc. IEEE), 2020
Robert Mansel Gower
Mark Schmidt
Francis R. Bach
Peter Richtárik
346
153
0
02 Oct 2020
Apollo: An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization
Xuezhe Ma
ODL
581
35
0
28 Sep 2020
Learning explanations that are hard to vary
International Conference on Learning Representations (ICLR), 2020
Giambattista Parascandolo
Alexander Neitz
Antonio Orvieto
Luigi Gresele
Bernhard Schölkopf
FAtt
498
220
0
01 Sep 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters
Filip Hanzely
242
0
0
26 Aug 2020
Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers
Philipp Geiger
C. Straehle
AI4CE
629
31
0
17 Aug 2020
Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods
Minghan Yang
Dong Xu
Yongfeng Li
Zaiwen Wen
Mengyun Chen
ODL
240
3
0
17 Jun 2020
Practical Quasi-Newton Methods for Training Deep Neural Networks
Shiqian Ma
Yi Ren
Achraf Bahamou
ODL
544
120
0
16 Jun 2020
Sketchy Empirical Natural Gradient Methods for Deep Learning
Minghan Yang
Dong Xu
Zaiwen Wen
Mengyun Chen
Pengxiang Xu
307
15
0
10 Jun 2020
Structure preserving deep learning
E. Celledoni
Matthias Joachim Ehrhardt
Christian Etmann
R. McLachlan
B. Owren
Carola-Bibiane Schönlieb
Ferdia Sherry
AI4CE
251
49
0
05 Jun 2020
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019
Ke Ma
Jinshan Zeng
Qianqian Xu
Xiaochun Cao
Wei Liu
Xingtai Lv
203
4
0
01 Dec 2019
A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization
Mathematical programming (Math. Program.), 2019
Minghan Yang
Andre Milzarek
Zaiwen Wen
Tong Zhang
ODL
244
38
0
21 Oct 2019
A Stochastic Variance Reduced Nesterov's Accelerated Quasi-Newton Method
International Conference on Machine Learning and Applications (ICMLA), 2019
Sota Yasuda
Shahrzad Mahboubi
S. Indrapriyadarsini
H. Ninomiya
H. Asai
ODL
71
7
0
17 Oct 2019
A Stochastic Quasi-Newton Method with Nesterov's Accelerated Gradient
S. Indrapriyadarsini
Shahrzad Mahboubi
H. Ninomiya
H. Asai
ODL
124
10
0
09 Sep 2019
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization
Journal of Optimization Theory and Applications (JOTA), 2019
Seonho Park
Seung Hyun Jung
P. Pardalos
ODL
221
20
0
27 Jun 2019
To Relieve Your Headache of Training an MRF, Take AdVIL
Chongxuan Li
Chao Du
Kun Xu
Max Welling
Jun Zhu
Bo Zhang
240
9
0
24 Jan 2019
A fast quasi-Newton-type method for large-scale stochastic optimisation
A. Wills
Carl Jidling
Thomas B. Schon
ODL
170
7
0
29 Sep 2018
1
2
Next
Page 1 of 2
