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A Multi-Batch L-BFGS Method for Machine Learning

19 May 2016

Papers citing "A Multi-Batch L-BFGS Method for Machine Learning"

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Title
Designing Preconditioners for SGD: Local Conditioning, Noise Floors, and Basin Stability Mitchell Scott Tianshi Xu Z. Tang Alexandra Pichette-Emmons Qiang Ye Y. Saad Yuanzhe Xi AI4CE 221 0 0 24 Nov 2025
PDLRecover: Privacy-preserving Decentralized Model Recovery with Machine Unlearning Xiangman Li Xiaodong Wu Jianbing Ni Mohamed Mahmoud Maazen Alsabaan AAML 123 0 0 18 Jun 2025
FUSE: First-Order and Second-Order Unified SynthEsis in Stochastic OptimizationConference on Algebraic Informatics (AI), 2025 Zhanhong Jiang Md Zahid Hasan Aditya Balu Joshua R. Waite Genyi Huang Soumik Sarkar 154 0 0 06 Mar 2025
Mobile Robotic Multi-View Photometric StereoIsprs Journal of Photogrammetry and Remote Sensing (ISPRS J. Photogramm. Remote Sens.), 2025 Suryansh Kumar 171 0 0 15 Feb 2025
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Jingruo Sun Zachary Frangella Madeleine Udell 178 2 0 28 Jan 2025
Knowledge Distillation with Adapted WeightStatistics (Berlin) (SB), 2025 Sirong Wu Junjie Liu Xi Luo Yuhui Deng 314 1 0 06 Jan 2025
Exact Gauss-Newton Optimization for Training Deep Neural Networks Mikalai Korbit Adeyemi Damilare Adeoye Alberto Bemporad Mario Zanon ODL 237 3 0 23 May 2024
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients Sachin Garg A. Berahas Michal Dereziñski 181 2 0 23 Apr 2024
Learning with SASQuaTCh: a Novel Variational Quantum Transformer Architecture with Kernel-Based Self-Attention Ethan N. Evans Matthew G. Cook Zachary P. Bradshaw Margarite L. LaBorde 316 9 0 21 Mar 2024
Parallel Trust-Region Approaches in Neural Network Training: Beyond Traditional Methods Ken Trotti Samuel A. Cruz Alegría Alena Kopanicáková Rolf Krause 176 1 0 21 Dec 2023
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization Siddharth Singh Zack Sating A. Bhatele ODL 172 0 0 18 Oct 2023
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale Hao-Jun Michael Shi Tsung-Hsien Lee Shintaro Iwasaki Jose Gallego-Posada Zhijing Li Kaushik Rangadurai Dheevatsa Mudigere Michael Rabbat ODL 200 43 0 12 Sep 2023
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems Abdeldjalil Latrach M. L. Malki Misael Morales Mohamed Mehana M. Rabiei PINN AI4CE 153 59 0 06 Aug 2023
On amortizing convex conjugates for optimal transportInternational Conference on Learning Representations (ICLR), 2022 Brandon Amos OT 276 31 0 21 Oct 2022
Component-Wise Natural Gradient Descent -- An Efficient Neural Network OptimizationInternational Symposium on Computing and Networking - Across Practical Development and Theoretical Research (ISAPDTR), 2022 Tran van Sang Mhd Irvan R. Yamaguchi Toshiyuki Nakata 162 1 0 11 Oct 2022
SP2: A Second Order Stochastic Polyak MethodInternational Conference on Learning Representations (ICLR), 2022 Shuang Li W. Swartworth Martin Takávc Deanna Needell Robert Mansel Gower 149 14 0 17 Jul 2022
Deep Leakage from Model in Federated Learning Zihao Zhao Mengen Luo Wenbo Ding FedML 110 18 0 10 Jun 2022
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel RecombinationNeural Information Processing Systems (NeurIPS), 2022 Masaki Adachi Satoshi Hayakawa Martin Jørgensen Harald Oberhauser Michael A. Osborne 252 25 0 09 Jun 2022
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning M. Yousefi Angeles Martinez ODL 102 1 0 18 May 2022
Data-aided Underwater Acoustic Ray Propagation ModelingIEEE Journal of Oceanic Engineering (IEEE J. Ocean. Eng.), 2022 Kexin Li M. Chitre 147 15 0 12 May 2022
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid RetrainingIEEE Conference on Computer Communications (INFOCOM), 2022 Yi Liu Lei Xu Lizhen Qu Cong Wang Bo Li MU 128 203 0 14 Mar 2022
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis Yi Liu Yuanshao Zhu James Jianqiao Yu FedML 143 29 0 14 Oct 2021
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic OptimizationMathematical Programming Computation (MPC), 2021 Raghu Bollapragada Stefan M. Wild 116 13 0 24 Sep 2021
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information Majid Jahani S. Rusakov Zheng Shi Peter Richtárik Michael W. Mahoney Martin Takávc ODL 142 29 0 11 Sep 2021
Quasi-Newton Quasi-Monte Carlo for variational Bayes Sifan Liu Art B. Owen BDL 150 4 0 07 Apr 2021
A Retrospective Approximation Approach for Smooth Stochastic OptimizationMathematics of Operations Research (MOR), 2021 David Newton Raghu Bollapragada R. Pasupathy N. Yip 221 3 0 07 Mar 2021
An Adaptive Memory Multi-Batch L-BFGS Algorithm for Neural Network TrainingIFAC-PapersOnLine (IFAC-PapersOnLine), 2020 Federico Zocco Seán F. McLoone ODL 103 5 0 14 Dec 2020
Asynchronous Parallel Stochastic Quasi-Newton MethodsParallel Computing (PC), 2020 Qianqian Tong Guannan Liang Xingyu Cai Chunjiang Zhu J. Bi ODL 182 10 0 02 Nov 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 195 0 0 26 Aug 2020
Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods Minghan Yang Dong Xu Yongfeng Li Zaiwen Wen Mengyun Chen ODL 143 3 0 17 Jun 2020
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm Majid Jahani M. Nazari R. Tappenden A. Berahas Martin Takávc ODL 117 10 0 06 Jun 2020
Stochastic Calibration of Radio Interferometers S. Yatawatta 145 6 0 02 Mar 2020
Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates D. Kovalev Konstantin Mishchenko Peter Richtárik ODL 158 51 0 03 Dec 2019
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization Raghu Bollapragada Stefan M. Wild 74 10 0 29 Oct 2019
A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex OptimizationMathematical programming (Math. Program.), 2019 Minghan Yang Andre Milzarek Zaiwen Wen Tong Zhang ODL 148 36 0 21 Oct 2019
Quasi-Newton Optimization Methods For Deep Learning ApplicationsAdvances in Intelligent Systems and Computing (AISC), 2019 J. Rafati Roummel F. Marcia ODL 116 15 0 04 Sep 2019
A Survey of Optimization Methods from a Machine Learning PerspectiveIEEE Transactions on Cybernetics (IEEE Trans. Cybern.), 2019 Shiliang Sun Zehui Cao Han Zhu Jing Zhao 181 618 0 17 Jun 2019
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1International Conference on Machine Learning, Optimization, and Data Science (MOD), 2019 Majid Jahani M. Nazari S. Rusakov A. Berahas Martin Takávc 186 16 0 30 May 2019
A Stochastic LBFGS Algorithm for Radio Interferometric Calibration S. Yatawatta Lukas De Clercq H. Spreeuw F. Diblen 147 8 0 11 Apr 2019
On the approximation of the solution of partial differential equations by artificial neural networks trained by a multilevel Levenberg-Marquardt method H. Calandra Serge Gratton E. Riccietti X. Vasseur 76 8 0 09 Apr 2019
Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample A. Berahas Majid Jahani Peter Richtárik Martin Takávc 285 48 0 28 Jan 2019
Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning Vinod Kumar Chauhan A. Sharma Kalpana Dahiya 211 7 0 26 Dec 2018
Deep Reinforcement Learning via L-BFGS Optimization Chris Paxton Roummel F. Marcia OffRL 149 0 0 06 Nov 2018
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy Majid Jahani Xi He Chenxin Ma Aryan Mokhtari Dheevatsa Mudigere Alejandro Ribeiro Martin Takáč 197 19 0 26 Oct 2018
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches Jie Liu Yu Rong Martin Takáč Junzhou Huang ODL 139 7 0 14 Jul 2018
Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations Jennifer B. Erway J. Griffin Roummel F. Marcia Riadh Omheni 290 26 0 01 Jul 2018
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization Umut Simsekli Çağatay Yıldız T. H. Nguyen G. Richard A. Cemgil 146 22 0 07 Jun 2018
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and LearningJournal of machine learning research (JMLR), 2018 C. Karakuş Yifan Sun Suhas Diggavi W. Yin 108 54 0 14 Mar 2018
A Progressive Batching L-BFGS Method for Machine Learning Raghu Bollapragada Dheevatsa Mudigere J. Nocedal Hao-Jun Michael Shi P. T. P. Tang ODL 174 164 0 15 Feb 2018
Straggler Mitigation in Distributed Optimization Through Data Encoding C. Karakuş Yifan Sun Suhas Diggavi W. Yin 135 150 0 14 Nov 2017