Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

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Title
Masked Training of Neural Networks with Partial Gradients Amirkeivan Mohtashami Martin Jaggi Sebastian U. Stich 27 22 0 16 Jun 2021
A Survey on Fault-tolerance in Distributed Optimization and Machine Learning Shuo Liu AI4CE OOD 58 13 0 16 Jun 2021
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control Amrit Singh Bedi Anjaly Parayil Junyu Zhang Mengdi Wang Alec Koppel 38 15 0 15 Jun 2021
RCURRENCY: Live Digital Asset Trading Using a Recurrent Neural Network-based Forecasting System Yapeng Hu R. V. Gurp Ashay Somai H. Kooijman Jan S. Rellermeyer 30 0 0 13 Jun 2021
Federated Learning with Buffered Asynchronous Aggregation John Nguyen Kshitiz Malik Hongyuan Zhan Ashkan Yousefpour Michael G. Rabbat Mani Malek Dzmitry Huba FedML 43 290 0 11 Jun 2021
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning Eugene Belilovsky Louis Leconte Lucas Caccia Michael Eickenberg Edouard Oyallon 24 7 0 11 Jun 2021
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs Vincent Plassier Maxime Vono Alain Durmus Eric Moulines 27 17 0 11 Jun 2021
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems Babak Barazandeh Tianjian Huang George Michailidis 37 12 0 10 Jun 2021
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip Mathieu Even Raphael Berthier Francis R. Bach Nicolas Flammarion Pierre Gaillard Hadrien Hendrikx Laurent Massoulié Adrien B. Taylor 13 20 0 10 Jun 2021
The dilemma of quantum neural networks Yan Qian Xinbiao Wang Yuxuan Du Xingyao Wu Dacheng Tao 21 30 0 09 Jun 2021
Asynchronous Distributed Optimization with Redundancy in Cost Functions Shuo Liu Nirupam Gupta Nitin H. Vaidya 31 3 0 07 Jun 2021
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models Courtney Paquette Elliot Paquette ODL 32 13 0 07 Jun 2021
Stein ICP for Uncertainty Estimation in Point Cloud Matching F. A. Maken Fabio Ramos Lionel Ott 3DV 3DPC 39 25 0 07 Jun 2021
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks Chaoyang He Emir Ceyani Keshav Balasubramanian M. Annavaram Salman Avestimehr FedML 25 50 0 04 Jun 2021
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning Blake E. Woodworth Nathan Srebro 25 21 0 04 Jun 2021
Stochastic gradient descent with noise of machine learning type. Part II: Continuous time analysis Stephan Wojtowytsch 39 33 0 04 Jun 2021
Debiasing a First-order Heuristic for Approximate Bi-level Optimization Valerii Likhosherstov Xingyou Song K. Choromanski Jared Davis Adrian Weller AI4CE 19 5 0 04 Jun 2021
Local Adaptivity in Federated Learning: Convergence and Consistency Jianyu Wang Zheng Xu Zachary Garrett Zachary B. Charles Luyang Liu Gauri Joshi FedML 37 39 0 04 Jun 2021
Improving Neural Network Robustness via Persistency of Excitation Kaustubh Sridhar O. Sokolsky Insup Lee James Weimer AAML 12 20 0 03 Jun 2021
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize Alain Durmus Eric Moulines A. Naumov S. Samsonov Kevin Scaman Hoi-To Wai 37 20 0 02 Jun 2021
Fine-grained Generalization Analysis of Structured Output Prediction Waleed Mustafa Yunwen Lei Antoine Ledent Marius Kloft 8 9 0 31 May 2021
Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective Kushal Chakrabarti Nikhil Chopra ODL AI4CE 40 9 0 31 May 2021
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent Anis Elgabli Chaouki Ben Issaid Amrit Singh Bedi M. Bennis Vaneet Aggarwal FedML 26 4 0 31 May 2021
Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations Mingfu Xue Yinghao Wu Zhiyu Wu Yushu Zhang Jian Wang Weiqiang Liu AAML 11 12 0 29 May 2021
A Stochastic Alternating Balance $k$ -Means Algorithm for Fair Clustering Suyun Liu Luis Nunes Vicente 30 11 0 29 May 2021
On Linear Stability of SGD and Input-Smoothness of Neural Networks Chao Ma Lexing Ying MLT 25 44 0 27 May 2021
SGD with Coordinate Sampling: Theory and Practice Rémi Leluc Franccois Portier 27 6 0 25 May 2021
Online Statistical Inference for Parameters Estimation with Linear-Equality Constraints Ruiqi Liu Mingao Yuan Zuofeng Shang 16 6 0 21 May 2021
A Measure of Research Taste V. Koltun David Hafner 25 1 0 17 May 2021
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning Yann Fraboni Richard Vidal Laetitia Kameni Marco Lorenzi FedML 21 184 0 12 May 2021
A Twin Neural Model for Uplift Mouloud Belbahri Olivier Gandouet A. Murua V. Nia CML 6 1 0 11 May 2021
A Geometric Analysis of Neural Collapse with Unconstrained Features Zhihui Zhu Tianyu Ding Jinxin Zhou Xiao Li Chong You Jeremias Sulam Qing Qu 40 197 0 06 May 2021
Stochastic gradient descent with noise of machine learning type. Part I: Discrete time analysis Stephan Wojtowytsch 25 50 0 04 May 2021
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning Xin Zhang Jia Liu Zhengyuan Zhu Elizabeth S. Bentley 51 14 0 04 May 2021
Convergence Analysis and System Design for Federated Learning over Wireless Networks Shuo Wan Jiaxun Lu Pingyi Fan Yunfeng Shao Chenghui Peng Khaled B. Letaief 47 54 0 30 Apr 2021
Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses Wenqiang Pu Shahana Ibrahim Xiao Fu Mingyi Hong 22 9 0 29 Apr 2021
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training Jianfei Chen Lianmin Zheng Z. Yao Dequan Wang Ion Stoica Michael W. Mahoney Joseph E. Gonzalez MQ 32 74 0 29 Apr 2021
Feasibility-based Fixed Point Networks Howard Heaton Samy Wu Fung A. Gibali W. Yin 29 26 0 29 Apr 2021
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning Yanjun Zhang Guangdong Bai Xue Li Surya Nepal R. Ko FedML 23 2 0 27 Apr 2021
Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions Michael C. Burkhart 14 0 0 27 Apr 2021
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data Yuchang Sun Jiawei Shao Yuyi Mao Jessie Hui Wang Jun Zhang FedML 46 39 0 26 Apr 2021
Solving a class of non-convex min-max games using adaptive momentum methods Babak Barazandeh Davoud Ataee Tarzanagh George Michailidis 37 13 0 26 Apr 2021
Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders Xinmeng Huang Kun Yuan Xianghui Mao W. Yin 35 12 0 25 Apr 2021
Random Reshuffling with Variance Reduction: New Analysis and Better Rates Grigory Malinovsky Alibek Sailanbayev Peter Richtárik 28 20 0 19 Apr 2021
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity J. Fuhg N. Bouklas PINN AI4CE 38 90 0 15 Apr 2021
Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCA Ying Cui Yangchen Li Chencheng Ye FedML 19 7 0 13 Apr 2021
Distributed Learning Systems with First-order Methods Ji Liu Ce Zhang 16 44 0 12 Apr 2021
SGD Implicitly Regularizes Generalization Error Daniel A. Roberts MLT 8 14 0 10 Apr 2021
Joint Optimization of Communications and Federated Learning Over the Air Xin-Yue Fan Yue Wang Yan Huo Z. Tian FedML 14 54 0 08 Apr 2021
Quasi-Newton Quasi-Monte Carlo for variational Bayes Sifan Liu Art B. Owen BDL 17 5 0 07 Apr 2021