Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

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Title
Training Deep Neural Networks via Branch-and-Bound Yuanwei Wu Ziming Zhang Guanghui Wang ODL 33 0 0 05 Apr 2021
Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise Thinh T. Doan 26 15 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 42 13 0 01 Apr 2021
Research of Damped Newton Stochastic Gradient Descent Method for Neural Network Training Jingcheng Zhou Wei Wei Zhiming Zheng ODL 14 0 0 31 Mar 2021
Multi-Source Causal Inference Using Control Variates Wenshuo Guo S. Wang Peng Ding Yixin Wang Michael I. Jordan CML 55 18 0 30 Mar 2021
Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning Subhayan De B. Hai Alireza Doostan M. Bause 22 2 0 30 Mar 2021
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design Lumin Liu Jun Zhang Shenghui Song Khaled B. Letaief FedML 47 80 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 43 12 0 24 Mar 2021
Adaptive deep density approximation for Fokker-Planck equations Keju Tang Xiaoliang Wan Qifeng Liao 31 37 0 20 Mar 2021
Distributed Deep Learning Using Volunteer Computing-Like Paradigm Medha Atre B. Jha Ashwini Rao 23 11 0 16 Mar 2021
Transient growth of accelerated optimization algorithms Hesameddin Mohammadi Samantha Samuelson M. Jovanović 17 8 0 14 Mar 2021
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget R. Saha Mert Pilanci Andrea J. Goldsmith 36 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 11 7 0 12 Mar 2021
EventGraD: Event-Triggered Communication in Parallel Machine Learning Soumyadip Ghosh B. Aquino V. Gupta FedML 26 8 0 12 Mar 2021
An Introduction to Deep Generative Modeling Lars Ruthotto E. Haber AI4CE 33 221 0 09 Mar 2021
On the Oracle Complexity of Higher-Order Smooth Non-Convex Finite-Sum Optimization N. Emmenegger Rasmus Kyng Ahad N. Zehmakan 13 2 0 08 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 29 109 0 08 Mar 2021
A Retrospective Approximation Approach for Smooth Stochastic Optimization David Newton Raghu Bollapragada R. Pasupathy N. Yip 35 2 0 07 Mar 2021
On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction Weilin Cong M. Ramezani M. Mahdavi 32 5 0 03 Mar 2021
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates Sebastian U. Stich Amirkeivan Mohtashami Martin Jaggi 17 22 0 03 Mar 2021
Deep Recurrent Encoder: A scalable end-to-end network to model brain signals O. Chehab Alexandre Défossez Jean-Christophe Loiseau Alexandre Gramfort J. King AI4TS 19 9 0 03 Mar 2021
Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning Hyun-Suk Lee Jang-Won Lee 81 53 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 43 253 0 26 Feb 2021
Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer Qunsong Zeng Yuqing Du Kaibin Huang 42 36 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 40 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 41 13 0 19 Feb 2021
Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm S. Khodadadian Zaiwei Chen S. T. Maguluri CML OffRL 74 26 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 33 30 0 16 Feb 2021
Learning by Turning: Neural Architecture Aware Optimisation Yang Liu Jeremy Bernstein M. Meister Yisong Yue ODL 48 26 0 14 Feb 2021
Newton Method over Networks is Fast up to the Statistical Precision Amir Daneshmand G. Scutari Pavel Dvurechensky Alexander Gasnikov 30 22 0 12 Feb 2021
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers Guojun Xiong Gang Yan Rahul Singh Jian Li 38 12 0 11 Feb 2021
An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians Sen Na M. Anitescu Mladen Kolar 27 41 0 10 Feb 2021
Attentive Gaussian processes for probabilistic time-series generation Kuilin Chen Chi-Guhn Lee AI4TS 16 1 0 10 Feb 2021
Consensus Control for Decentralized Deep Learning Lingjing Kong Tao R. Lin Anastasia Koloskova Martin Jaggi Sebastian U. Stich 19 76 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 28 101 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 40 23 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 24 108 0 08 Feb 2021
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality Courtney Paquette Kiwon Lee Fabian Pedregosa Elliot Paquette 17 32 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 26 85 0 05 Feb 2021
Local Critic Training for Model-Parallel Learning of Deep Neural Networks Hojung Lee Cho-Jui Hsieh Jong-Seok Lee 36 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 21 47 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 105 54 0 02 Feb 2021
Stochastic Online Convex Optimization. Application to probabilistic time series forecasting Olivier Wintenberger AI4TS 32 5 0 01 Feb 2021
Parameter-free Stochastic Optimization of Variationally Coherent Functions Francesco Orabona Dávid Pál 32 17 0 30 Jan 2021
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training Cong Fang Hangfeng He Qi Long Weijie J. Su FAtt 132 168 0 29 Jan 2021
Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent Nirupam Gupta Nitin H. Vaidya 23 15 0 28 Jan 2021
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning Haibo Yang Minghong Fang Jia Liu FedML 28 251 0 27 Jan 2021
SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees Jiaming Liu Yu Sun Weijie Gan Xiaojian Xu B. Wohlberg Ulugbek S. Kamilov FedML MedIm 34 30 0 22 Jan 2021
Approximate Byzantine Fault-Tolerance in Distributed Optimization Shuo Liu Nirupam Gupta Nitin H. Vaidya 33 42 0 22 Jan 2021
Gravity Optimizer: a Kinematic Approach on Optimization in Deep Learning Dariush Bahrami Sadegh Pouriyan Zadeh ODL 9 5 0 22 Jan 2021