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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 Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence Nicolas Loizou Sharan Vaswani I. Laradji Simon Lacoste-Julien 105 189 0 24 Feb 2020
The Two Regimes of Deep Network Training Guillaume Leclerc Aleksander Madry 94 45 0 24 Feb 2020
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems Hans Kersting N. Krämer Martin Schiegg Christian Daniel Michael Tiemann Philipp Hennig 70 21 0 21 Feb 2020
Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient descent I. Ayadi Gabriel Turinici ODL 35 9 0 20 Feb 2020
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets Ilqar Ramazanli Han Nguyen Hai Pham Sashank J. Reddi Barnabás Póczós 83 11 0 20 Feb 2020
A Unified Convergence Analysis for Shuffling-Type Gradient Methods Lam M. Nguyen Quoc Tran-Dinh Dzung Phan Phuong Ha Nguyen Marten van Dijk 106 79 0 19 Feb 2020
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis Jung Yeon Park K. T. Carr Stephan Zhang Yisong Yue Rose Yu 109 14 0 13 Feb 2020
Gradient tracking and variance reduction for decentralized optimization and machine learning Ran Xin S. Kar U. Khan 49 10 0 13 Feb 2020
RFN: A Random-Feature Based Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Spaces Ting-Jui Chang Shahin Shahrampour 67 2 0 12 Feb 2020
On the distance between two neural networks and the stability of learning Jeremy Bernstein Arash Vahdat Yisong Yue Xuan Li ODL 281 59 0 09 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 109 187 0 09 Feb 2020
Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex Optimization Thomas O'Leary-Roseberry Nick Alger Omar Ghattas ODL 70 9 0 07 Feb 2020
Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter -- a Case Study M. Hotvedt B. Grimstad Lars Imsland 43 22 0 07 Feb 2020
Differentially Quantized Gradient Methods Chung-Yi Lin V. Kostina B. Hassibi MQ 66 8 0 06 Feb 2020
Faster On-Device Training Using New Federated Momentum Algorithm Zhouyuan Huo Qian Yang Bin Gu Heng-Chiao Huang FedML 195 47 0 06 Feb 2020
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes Zaiwei Chen S. T. Maguluri Sanjay Shakkottai Karthikeyan Shanmugam 141 33 0 03 Feb 2020
Replica Exchange for Non-Convex Optimization Jing-rong Dong Xin T. Tong 108 21 0 23 Jan 2020
Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning Yining Qi Zhihao Qu Song Guo Xin Gao Ruixuan Li Baoliu Ye FedML 45 9 0 22 Jan 2020
A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis Christopher J. Urban Daniel J. Bauer BDL 81 33 0 22 Jan 2020
Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem Kun He Min Zhang Jianrong Zhou Yan Jin ChuMin Li 13 2 0 22 Jan 2020
Adaptive Stochastic Optimization Frank E. Curtis K. Scheinberg ODL 48 29 0 18 Jan 2020
Learning the Ising Model with Generative Neural Networks Francesco DÁngelo Lucas Böttcher AI4CE 57 28 0 15 Jan 2020
Secure multiparty computations in floating-point arithmetic Chuan Guo Awni Y. Hannun Brian Knott Laurens van der Maaten M. Tygert Ruiyu Zhu FedML 55 17 0 09 Jan 2020
Distributionally Robust Deep Learning using Hardness Weighted Sampling Lucas Fidon Michael Aertsen Thomas Deprest Doaa Emam Frédéric Guffens ... Andrew Melbourne Sébastien Ourselin Jan Deprest Georg Langs Tom Vercauteren OOD 96 10 0 08 Jan 2020
Stochastic gradient-free descents Xiaopeng Luo Xin Xu ODL 23 2 0 31 Dec 2019
Characterizing the Decision Boundary of Deep Neural Networks Hamid Karimi Hanyu Wang Jiliang Tang 91 67 0 24 Dec 2019
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation Thinh T. Doan 82 36 0 23 Dec 2019
Second-order Information in First-order Optimization Methods Yuzheng Hu Licong Lin Shange Tang ODL 53 2 0 20 Dec 2019
Learning Convex Optimization Control Policies Akshay Agrawal Shane T. Barratt Stephen P. Boyd Bartolomeo Stellato 80 70 0 19 Dec 2019
Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning Nirupam Gupta Nitin H. Vaidya FedML 79 8 0 19 Dec 2019
Optimization for deep learning: theory and algorithms Ruoyu Sun ODL 137 169 0 19 Dec 2019
PyHessian: Neural Networks Through the Lens of the Hessian Z. Yao A. Gholami Kurt Keutzer Michael W. Mahoney ODL 89 305 0 16 Dec 2019
A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems Lars Ruthotto Stanley Osher Wuchen Li L. Nurbekyan Samy Wu Fung AI4CE 180 220 0 04 Dec 2019
Federated Learning with Personalization Layers Manoj Ghuhan Arivazhagan V. Aggarwal Aaditya Kumar Singh Sunav Choudhary FedML 101 853 0 02 Dec 2019
Automatic Differentiable Monte Carlo: Theory and Application Shi-Xin Zhang Z. Wan H. Yao 57 17 0 20 Nov 2019
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees Shaoshuai Shi Zhenheng Tang Qiang-qiang Wang Kaiyong Zhao Xiaowen Chu 65 22 0 20 Nov 2019
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning Aritra Dutta El Houcine Bergou A. Abdelmoniem Chen-Yu Ho Atal Narayan Sahu Marco Canini Panos Kalnis 77 78 0 19 Nov 2019
Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization Anas Barakat Pascal Bianchi 77 12 0 18 Nov 2019
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data Yujing Chen Yue Ning Martin Slawski Huzefa Rangwala FedML 101 56 0 05 Nov 2019
A Rule for Gradient Estimator Selection, with an Application to Variational Inference Tomas Geffner Justin Domke 66 6 0 05 Nov 2019
On the Convergence of Local Descent Methods in Federated Learning Farzin Haddadpour M. Mahdavi FedML 93 275 0 31 Oct 2019
Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation Beidi Chen Yingchen Xu Anshumali Shrivastava 70 16 0 30 Oct 2019
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization Farzin Haddadpour Mohammad Mahdi Kamani M. Mahdavi V. Cadambe FedML 87 202 0 30 Oct 2019
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks Jonathan Ephrath Moshe Eliasof Lars Ruthotto E. Haber Eran Treister 140 17 0 29 Oct 2019
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels Angus Dempster Franccois Petitjean Geoffrey I. Webb AI4TS 91 785 0 29 Oct 2019
Non-Gaussianity of Stochastic Gradient Noise A. Panigrahi Raghav Somani Navin Goyal Praneeth Netrapalli 68 53 0 21 Oct 2019
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD Rosa Candela Giulio Franzese Maurizio Filippone Pietro Michiardi 91 1 0 21 Oct 2019
A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization Minghan Yang Andre Milzarek Zaiwen Wen Tong Zhang ODL 96 36 0 21 Oct 2019
Communication-Efficient Local Decentralized SGD Methods Xiang Li Wenhao Yang Shusen Wang Zhihua Zhang 97 53 0 21 Oct 2019
A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning Yasaman Esfandiari Aditya Balu K. Ebrahimi Umesh Vaidya N. Elia Soumik Sarkar OOD 59 3 0 18 Oct 2019