Global Convergence of Online Limited Memory BFGS

6 September 2014

Alejandro Ribeiro

Papers citing "Global Convergence of Online Limited Memory BFGS"

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Title
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mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization Yue Niu Zalan Fabian Sunwoo Lee Mahdi Soltanolkotabi Salman Avestimehr ODL 17 2 0 25 Jul 2023
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate Zhan Gao Aryan Mokhtari Alec Koppel 15 2 0 27 Jun 2023
Machine Unlearning: A Survey Heng Xu Tianqing Zhu Lefeng Zhang Wanlei Zhou Philip S. Yu MU 41 19 0 06 Jun 2023
Sharpened Lazy Incremental Quasi-Newton Method Aakash Lahoti Spandan Senapati K. Rajawat Alec Koppel 32 2 0 26 May 2023
Online Learning Under A Separable Stochastic Approximation Framework Min Gan Xiang-Xiang Su Guang-yong Chen Jing Chen 28 0 0 12 May 2023
Provably Convergent Plug-and-Play Quasi-Newton Methods Hongwei Tan Subhadip Mukherjee Junqi Tang Carola-Bibiane Schönlieb 34 13 0 09 Mar 2023
SP2: A Second Order Stochastic Polyak Method Shuang Li W. Swartworth Martin Takávc Deanna Needell Robert Mansel Gower 26 13 0 17 Jul 2022
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning M. Yousefi Angeles Martinez ODL 16 1 0 18 May 2022
Optimization for Classical Machine Learning Problems on the GPU Soren Laue Mark Blacher Joachim Giesen 6 5 0 30 Mar 2022
Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part I Jiaojiao Zhang Huikang Liu Anthony Man-Cho So Qing Ling 24 14 0 19 Jan 2022
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 24 25 0 11 Sep 2021
L-DQN: An Asynchronous Limited-Memory Distributed Quasi-Newton Method Bugra Can Saeed Soori M. Dehnavi Mert Gurbuzbalaban 40 2 0 20 Aug 2021
Memory Augmented Optimizers for Deep Learning Paul-Aymeric McRae Prasanna Parthasarathi Mahmoud Assran Sarath Chandar ODL 30 3 0 20 Jun 2021
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach Qiujiang Jin Aryan Mokhtari 14 4 0 10 Jun 2021
A Retrospective Approximation Approach for Smooth Stochastic Optimization David Newton Raghu Bollapragada R. Pasupathy N. Yip 35 2 0 07 Mar 2021
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? Antoine Grosnit Alexander I. Cowen-Rivers Rasul Tutunov Ryan-Rhys Griffiths Jun Wang Haitham Bou-Ammar 19 13 0 15 Dec 2020
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory Amirali Aghazadeh Vipul Gupta Alex DeWeese O. O. Koyluoglu Kannan Ramchandran 22 2 0 26 Oct 2020
An adaptive Hessian approximated stochastic gradient MCMC method Yating Wang Wei Deng Guang Lin BDL 13 5 0 03 Oct 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark W. Schmidt Francis R. Bach Peter Richtárik 19 111 0 02 Oct 2020
Apollo: An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization Xuezhe Ma ODL 34 31 0 28 Sep 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 37 0 0 26 Aug 2020
DeltaGrad: Rapid retraining of machine learning models Yinjun Wu Yan Sun S. Davidson MU 25 196 0 26 Jun 2020
Practical Quasi-Newton Methods for Training Deep Neural Networks D. Goldfarb Yi Ren Achraf Bahamou ODL 16 104 0 16 Jun 2020
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm Majid Jahani M. Nazari R. Tappenden A. Berahas Martin Takávc ODL 19 10 0 06 Jun 2020
RFN: A Random-Feature Based Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Spaces Ting-Jui Chang Shahin Shahrampour 17 2 0 12 Feb 2020
A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization Minghan Yang Andre Milzarek Zaiwen Wen Tong Zhang ODL 17 36 0 21 Oct 2019
A Stochastic Quasi-Newton Method with Nesterov's Accelerated Gradient S. Indrapriyadarsini Shahrzad Mahboubi H. Ninomiya H. Asai ODL 14 10 0 09 Sep 2019
A Survey of Optimization Methods from a Machine Learning Perspective Shiliang Sun Zehui Cao Han Zhu Jing Zhao 22 549 0 17 Jun 2019
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1 Majid Jahani M. Nazari S. Rusakov A. Berahas Martin Takávc 20 14 0 30 May 2019
Learning Networked Exponential Families with Network Lasso A. Jung 14 2 0 22 May 2019
Accurate and Robust Alignment of Variable-stained Histologic Images Using a General-purpose Greedy Diffeomorphic Registration Tool Ludovic Venet Sarthak Pati Paul Yushkevich Spyridon Bakas 13 10 0 26 Apr 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
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 22 18 0 26 Oct 2018
A fast quasi-Newton-type method for large-scale stochastic optimisation A. Wills Carl Jidling Thomas B. Schon ODL 33 7 0 29 Sep 2018
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization Aryan Mokhtari Hamed Hassani Amin Karbasi 23 111 0 24 Apr 2018
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning C. Karakuş Yifan Sun Suhas Diggavi W. Yin 18 52 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 11 152 0 15 Feb 2018
Stochastic quasi-Newton with adaptive step lengths for large-scale problems A. Wills Thomas B. Schon 35 9 0 12 Feb 2018
Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients Tengyuan Liang Weijie Su 19 21 0 20 Dec 2017
Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks Shankar Krishnan Ying Xiao Rif A. Saurous ODL 22 19 0 08 Dec 2017
Straggler Mitigation in Distributed Optimization Through Data Encoding C. Karakuş Yifan Sun Suhas Diggavi W. Yin 20 142 0 14 Nov 2017
SGDLibrary: A MATLAB library for stochastic gradient descent algorithms Hiroyuki Kasai 21 3 0 27 Oct 2017
Fast online low-rank tensor subspace tracking by CP decomposition using recursive least squares from incomplete observations Hiroyuki Kasai 19 27 0 29 Sep 2017
A Robust Multi-Batch L-BFGS Method for Machine Learning A. Berahas Martin Takáč AAML ODL 25 44 0 26 Jul 2017
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method Mark Eisen Aryan Mokhtari Alejandro Ribeiro 24 16 0 22 May 2017
Accelerated Stochastic Quasi-Newton Optimization on Riemann Manifolds A. Roychowdhury 19 4 0 06 Apr 2017
Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies Renbo Zhao W. Haskell Vincent Y. F. Tan 17 29 0 01 Apr 2017
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis Hiroyuki Kasai Hiroyuki Sato Bamdev Mishra 16 22 0 15 Mar 2017