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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
Stochastic gradient descent with noise of machine learning type. Part I: Discrete time analysis Stephan Wojtowytsch 76 52 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 84 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 82 55 0 30 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 77 75 0 29 Apr 2021
Feasibility-based Fixed Point Networks Howard Heaton Samy Wu Fung A. Gibali W. Yin 57 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 32 2 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 111 39 0 26 Apr 2021
Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders Xinmeng Huang Kun Yuan Xianghui Mao W. Yin 64 13 0 25 Apr 2021
Random Reshuffling with Variance Reduction: New Analysis and Better Rates Grigory Malinovsky Alibek Sailanbayev Peter Richtárik 56 21 0 19 Apr 2021
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity J. Fuhg N. Bouklas PINN AI4CE 81 95 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 36 7 0 13 Apr 2021
Distributed Learning Systems with First-order Methods Ji Liu Ce Zhang 36 44 0 12 Apr 2021
Joint Optimization of Communications and Federated Learning Over the Air Xin-Yue Fan Yue Wang Yan Huo Z. Tian FedML 56 55 0 08 Apr 2021
Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise Thinh T. Doan 94 16 0 04 Apr 2021
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions Arnulf Jentzen Adrian Riekert MLT 86 13 0 01 Apr 2021
Multi-Source Causal Inference Using Control Variates Wenshuo Guo S. Wang Peng Ding Yixin Wang Michael I. Jordan CML 101 19 0 30 Mar 2021
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design Lumin Liu Jun Zhang Shenghui Song Khaled B. Letaief FedML 70 87 0 26 Mar 2021
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication Xing Xu Rongpeng Li Zhifeng Zhao Honggang Zhang 84 12 0 24 Mar 2021
Adaptive deep density approximation for Fokker-Planck equations Keju Tang Xiaoliang Wan Qifeng Liao 93 40 0 20 Mar 2021
Distributed Deep Learning Using Volunteer Computing-Like Paradigm Medha Atre B. Jha Ashwini Rao 91 11 0 16 Mar 2021
Transient growth of accelerated optimization algorithms Hesameddin Mohammadi Samantha Samuelson M. Jovanović 32 8 0 14 Mar 2021
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget R. Saha Mert Pilanci Andrea J. Goldsmith 100 5 0 13 Mar 2021
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training Adnan Haider Chao Zhang Florian Kreyssig P. Woodland 112 7 0 12 Mar 2021
EventGraD: Event-Triggered Communication in Parallel Machine Learning Soumyadip Ghosh B. Aquino V. Gupta FedML 101 9 0 12 Mar 2021
An Introduction to Deep Generative Modeling Lars Ruthotto E. Haber AI4CE 118 232 0 09 Mar 2021
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie R. Laumont Valentin De Bortoli Andrés Almansa J. Delon Alain Durmus Marcelo Pereyra 96 112 0 08 Mar 2021
On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction Weilin Cong M. Ramezani M. Mahdavi 62 5 0 03 Mar 2021
Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning Hyun-Suk Lee Jang-Won Lee 139 55 0 02 Mar 2021
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability Jeremy M. Cohen Simran Kaur Yuanzhi Li J. Zico Kolter Ameet Talwalkar ODL 133 279 0 26 Feb 2021
Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer Qunsong Zeng Yuqing Du Kaibin Huang 92 37 0 24 Feb 2021
Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization Jyun-Li Lin Wei-Ting Hung Shangtong Yang Ping-Chun Hsieh Xi Liu 110 14 0 22 Feb 2021
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods Zheng Shi Abdurakhmon Sadiev Nicolas Loizou Peter Richtárik Martin Takávc ODL 84 13 0 19 Feb 2021
Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm S. Khodadadian Zaiwei Chen S. T. Maguluri CML OffRL 137 27 0 18 Feb 2021
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? R. Guerraoui Nirupam Gupta Rafael Pinot Sébastien Rouault John Stephan 76 30 0 16 Feb 2021
Learning by Turning: Neural Architecture Aware Optimisation Yang Liu Jeremy Bernstein M. Meister Yisong Yue ODL 127 26 0 14 Feb 2021
Newton Method over Networks is Fast up to the Statistical Precision Amir Daneshmand G. Scutari Pavel Dvurechensky Alexander Gasnikov 72 22 0 12 Feb 2021
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers Guojun Xiong Gang Yan Rahul Singh Jian Li 65 13 0 11 Feb 2021
An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians Sen Na M. Anitescu Mladen Kolar 85 44 0 10 Feb 2021
Consensus Control for Decentralized Deep Learning Lingjing Kong Tao R. Lin Anastasia Koloskova Martin Jaggi Sebastian U. Stich 53 80 0 09 Feb 2021
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data Tao R. Lin Sai Praneeth Karimireddy Sebastian U. Stich Martin Jaggi FedML 107 101 0 09 Feb 2021
Large-Scale Training System for 100-Million Classification at Alibaba Liuyihan Song Pan Pan Kang Zhao Hao Yang Yiming Chen Yingya Zhang Yinghui Xu Rong Jin 86 24 0 09 Feb 2021
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning Divyansh Jhunjhunwala Advait Gadhikar Gauri Joshi Yonina C. Eldar FedML MQ 76 112 0 08 Feb 2021
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality Courtney Paquette Kiwon Lee Fabian Pedregosa Elliot Paquette 59 35 0 08 Feb 2021
Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles Tengchan Zeng Omid Semiariy Mingzhe Chen Walid Saad M. Bennis FedML 61 86 0 05 Feb 2021
Local Critic Training for Model-Parallel Learning of Deep Neural Networks Hojung Lee Cho-Jui Hsieh Jong-Seok Lee 63 15 0 03 Feb 2021
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication Blake E. Woodworth Brian Bullins Ohad Shamir Nathan Srebro 67 49 0 02 Feb 2021
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants Zaiwei Chen S. T. Maguluri Sanjay Shakkottai Karthikeyan Shanmugam OffRL 213 55 0 02 Feb 2021
Stochastic Online Convex Optimization. Application to probabilistic time series forecasting Olivier Wintenberger AI4TS 107 8 0 01 Feb 2021
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training Cong Fang Hangfeng He Qi Long Weijie J. Su FAtt 203 172 0 29 Jan 2021
Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent Nirupam Gupta Nitin H. Vaidya 66 15 0 28 Jan 2021