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Optimization Methods for Large-Scale Machine Learning

15 June 2016

Papers citing "Optimization Methods for Large-Scale Machine Learning"

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Title
Efficient Subsampled Gauss-Newton and Natural Gradient Methods for Training Neural Networks Yi Ren Shiqian Ma 64 37 0 05 Jun 2019
On the Convergence of SARAH and Beyond Bingcong Li Meng Ma G. Giannakis 70 27 0 05 Jun 2019
Approximate Inference Turns Deep Networks into Gaussian Processes Mohammad Emtiyaz Khan Alexander Immer Ehsan Abedi M. Korzepa UQCV BDL 132 125 0 05 Jun 2019
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis Cynthia Rudin David Carlson HAI 128 34 0 04 Jun 2019
A Generic Acceleration Framework for Stochastic Composite Optimization A. Kulunchakov Julien Mairal 105 43 0 03 Jun 2019
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1 Majid Jahani M. Nazari S. Rusakov A. Berahas Martin Takávc 75 14 0 30 May 2019
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent Frederik Kunstner Lukas Balles Philipp Hennig 101 219 0 29 May 2019
An Inertial Newton Algorithm for Deep Learning Camille Castera Jérôme Bolte Cédric Févotte Edouard Pauwels PINN ODL 117 64 0 29 May 2019
Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization Yifan Hu Xin Chen Niao He 91 36 0 28 May 2019
Recursive Estimation for Sparse Gaussian Process Regression Manuel Schürch Dario Azzimonti A. Benavoli Marco Zaffalon 65 33 0 28 May 2019
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning Zaiwei Chen Sheng Zhang Thinh T. Doan John-Paul Clarke S. T. Maguluri 146 59 0 27 May 2019
Robustness of accelerated first-order algorithms for strongly convex optimization problems Hesameddin Mohammadi Meisam Razaviyayn M. Jovanović 33 41 0 27 May 2019
Decentralized Bayesian Learning over Graphs Anusha Lalitha Xinghan Wang O. Kilinc Y. Lu T. Javidi F. Koushanfar FedML 73 25 0 24 May 2019
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension Yunfei Teng Wenbo Gao F. Chalus A. Choromańska Shiqian Ma Adrian Weller 142 12 0 24 May 2019
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling Jianyu Wang Anit Kumar Sahu Zhouyi Yang Gauri Joshi S. Kar 96 163 0 23 May 2019
LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning Jingjing Zhang Osvaldo Simeone 72 32 0 22 May 2019
Client-Edge-Cloud Hierarchical Federated Learning Lumin Liu Jun Zhang S. H. Song Khaled B. Letaief FedML 93 758 0 16 May 2019
A Stochastic Gradient Method with Biased Estimation for Faster Nonconvex Optimization Jia Bi S. Gunn 61 3 0 13 May 2019
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints Mengtian Li Ersin Yumer Deva Ramanan 72 49 0 12 May 2019
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization Hao Yu Rong Jin 82 51 0 10 May 2019
Sparse multiresolution representations with adaptive kernels Maria Peifer Luiz F. O. Chamon Santiago Paternain Alejandro Ribeiro 48 4 0 07 May 2019
An Adaptive Remote Stochastic Gradient Method for Training Neural Networks Yushu Chen Hao Jing Wenlai Zhao Zhiqiang Liu Haohuan Fu Lián Qiao Wei Xue Guangwen Yang ODL 92 2 0 04 May 2019
Target-Based Temporal Difference Learning Donghwan Lee Niao He OOD 83 32 0 24 Apr 2019
Least Squares Auto-Tuning Shane T. Barratt Stephen P. Boyd MoMe 82 23 0 10 Apr 2019
Generalizing from a Few Examples: A Survey on Few-Shot Learning Yaqing Wang Quanming Yao James T. Kwok L. Ni 147 1,849 0 10 Apr 2019
Convergence rates for the stochastic gradient descent method for non-convex objective functions Benjamin J. Fehrman Benjamin Gess Arnulf Jentzen 98 101 0 02 Apr 2019
Convergence rates for optimised adaptive importance samplers Ömer Deniz Akyildiz Joaquín Míguez 133 31 0 28 Mar 2019
OverSketched Newton: Fast Convex Optimization for Serverless Systems Vipul Gupta S. Kadhe T. Courtade Michael W. Mahoney Kannan Ramchandran 85 33 0 21 Mar 2019
Noisy Accelerated Power Method for Eigenproblems with Applications Vien V. Mai M. Johansson 30 3 0 20 Mar 2019
TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applications Frederik Heber Zofia Trstanova Benedict Leimkuhler 40 1 0 20 Mar 2019
A Distributed Hierarchical SGD Algorithm with Sparse Global Reduction Fan Zhou Guojing Cong 65 8 0 12 Mar 2019
SGD without Replacement: Sharper Rates for General Smooth Convex Functions Prateek Jain Dheeraj M. Nagaraj Praneeth Netrapalli 90 87 0 04 Mar 2019
Time-Delay Momentum: A Regularization Perspective on the Convergence and Generalization of Stochastic Momentum for Deep Learning Ziming Zhang Wenju Xu Alan Sullivan 99 1 0 02 Mar 2019
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise Yeming Wen Kevin Luk Maxime Gazeau Guodong Zhang Harris Chan Jimmy Ba ODL 73 22 0 21 Feb 2019
Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network Xiaoxia Wu S. Du Rachel A. Ward 103 66 0 19 Feb 2019
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization Nhan H. Pham Lam M. Nguyen Dzung Phan Quoc Tran-Dinh 80 141 0 15 Feb 2019
Forward-backward-forward methods with variance reduction for stochastic variational inequalities R. Boț P. Mertikopoulos Mathias Staudigl P. Vuong 68 23 0 09 Feb 2019
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks P. Parpas Corey Muir OOD 88 12 0 07 Feb 2019
Negative eigenvalues of the Hessian in deep neural networks Guillaume Alain Nicolas Le Roux Pierre-Antoine Manzagol 76 44 0 06 Feb 2019
Riemannian adaptive stochastic gradient algorithms on matrix manifolds Hiroyuki Kasai Pratik Jawanpuria Bamdev Mishra 84 3 0 04 Feb 2019
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions Adrien B. Taylor Francis R. Bach 79 64 0 03 Feb 2019
Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions Yunwen Lei Ting Hu Guiying Li K. Tang MLT 95 119 0 03 Feb 2019
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme Belhal Karimi B. Miasojedow Eric Moulines Hoi-To Wai 100 91 0 02 Feb 2019
Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning Nicolas García Trillos Zachary T. Kaplan D. Sanz-Alonso ODL 57 3 0 29 Jan 2019
Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample A. Berahas Majid Jahani Peter Richtárik Martin Takávc 107 41 0 28 Jan 2019
SGD: General Analysis and Improved Rates Robert Mansel Gower Nicolas Loizou Xun Qian Alibek Sailanbayev Egor Shulgin Peter Richtárik 109 383 0 27 Jan 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 90 45 0 25 Jan 2019
Provable Smoothness Guarantees for Black-Box Variational Inference Justin Domke 74 36 0 24 Jan 2019
Large-Batch Training for LSTM and Beyond Yang You Jonathan Hseu Chris Ying J. Demmel Kurt Keutzer Cho-Jui Hsieh 65 91 0 24 Jan 2019
Decoupled Greedy Learning of CNNs Eugene Belilovsky Michael Eickenberg Edouard Oyallon 80 117 0 23 Jan 2019