Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

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Title
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings Mahmoud Assran Michael G. Rabbat 19 59 0 27 Feb 2020
A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow Ruthvik Vaila John N. Chiasson V. Saxena 28 22 0 26 Feb 2020
Disentangling Adaptive Gradient Methods from Learning Rates Naman Agarwal Rohan Anil Elad Hazan Tomer Koren Cyril Zhang 27 34 0 26 Feb 2020
PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models Yinjun Wu V. Tannen S. Davidson 6 37 0 26 Feb 2020
Non-asymptotic bounds for stochastic optimization with biased noisy gradient oracles Nirav Bhavsar Prashanth L.A. 16 11 0 26 Feb 2020
LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning Tianyi Chen Yuejiao Sun W. Yin FedML 22 14 0 26 Feb 2020
Device Heterogeneity in Federated Learning: A Superquantile Approach Yassine Laguel Krishna Pillutla J. Malick Zaïd Harchaoui FedML 45 22 0 25 Feb 2020
Adaptive Distributed Stochastic Gradient Descent for Minimizing Delay in the Presence of Stragglers Serge Kas Hanna Rawad Bitar Parimal Parag Venkateswara Dasari S. E. Rouayheb 27 16 0 25 Feb 2020
Layer-wise Conditioning Analysis in Exploring the Learning Dynamics of DNNs Lei Huang Jie Qin Li Liu Fan Zhu Ling Shao AI4CE 31 11 0 25 Feb 2020
$Can speed up the convergence rate of stochastic gradient methods to $\mathcal{O}(1/k^2)$ by a gradient averaging strategy?$ Can speed up the convergence rate of stochastic gradient methods to $\mathcal{O}(1/k^2)$ by a gradient averaging strategy? Xin Xu Xiaopeng Luo 21 1 0 25 Feb 2020
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent Bao Wang T. Nguyen Andrea L. Bertozzi Richard G. Baraniuk Stanley J. Osher ODL 9 48 0 24 Feb 2020
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence Nicolas Loizou Sharan Vaswani I. Laradji Simon Lacoste-Julien 29 182 0 24 Feb 2020
The Two Regimes of Deep Network Training Guillaume Leclerc Aleksander Madry 27 45 0 24 Feb 2020
Periodic Q-Learning Dong-hwan Lee Niao He OOD 17 13 0 23 Feb 2020
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems Hans Kersting N. Krämer Martin Schiegg Christian Daniel Michael Tiemann Philipp Hennig 35 21 0 21 Feb 2020
Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient descent I. Ayadi Gabriel Turinici ODL 9 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 25 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 44 78 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 38 14 0 13 Feb 2020
Stochastic Approximate Gradient Descent via the Langevin Algorithm Yixuan Qiu Tianlin Li 30 4 0 13 Feb 2020
Gradient tracking and variance reduction for decentralized optimization and machine learning Ran Xin S. Kar U. Khan 19 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 22 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 200 57 0 09 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 20 180 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 42 9 0 07 Feb 2020
Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter -- a Case Study M. Hotvedt B. Grimstad Lars Imsland 27 22 0 07 Feb 2020
Differentially Quantized Gradient Methods Chung-Yi Lin V. Kostina B. Hassibi MQ 30 7 0 06 Feb 2020
Almost Sure Convergence of Dropout Algorithms for Neural Networks Albert Senen-Cerda J. Sanders 34 8 0 06 Feb 2020
Faster On-Device Training Using New Federated Momentum Algorithm Zhouyuan Huo Qian Yang Bin Gu Heng-Chiao Huang FedML 24 47 0 06 Feb 2020
Large Batch Training Does Not Need Warmup Zhouyuan Huo Bin Gu Heng-Chiao Huang AI4CE ODL 29 5 0 04 Feb 2020
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes Zaiwei Chen S. T. Maguluri Sanjay Shakkottai Karthikeyan Shanmugam 61 33 0 03 Feb 2020
Replica Exchange for Non-Convex Optimization Jing-rong Dong Xin T. Tong 32 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 18 9 0 22 Jan 2020
A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis Christopher J. Urban Daniel J. Bauer BDL 18 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 6 2 0 22 Jan 2020
Adaptive Stochastic Optimization Frank E. Curtis K. Scheinberg ODL 21 29 0 18 Jan 2020
Learning the Ising Model with Generative Neural Networks Francesco DÁngelo Lucas Böttcher AI4CE 16 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 13 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 31 10 0 08 Jan 2020
Stochastic gradient-free descents Xiaopeng Luo Xin Xu ODL 14 2 0 31 Dec 2019
Characterizing the Decision Boundary of Deep Neural Networks Hamid Karimi Tyler Derr Jiliang Tang 22 65 0 24 Dec 2019
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation Thinh T. Doan 21 36 0 23 Dec 2019
Second-order Information in First-order Optimization Methods Yuzheng Hu Licong Lin Shange Tang ODL 30 2 0 20 Dec 2019
Learning Convex Optimization Control Policies Akshay Agrawal Shane T. Barratt Stephen P. Boyd Bartolomeo Stellato 35 66 0 19 Dec 2019
Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning Nirupam Gupta Nitin H. Vaidya FedML 38 8 0 19 Dec 2019
Optimization for deep learning: theory and algorithms Ruoyu Sun ODL 43 168 0 19 Dec 2019
PyHessian: Neural Networks Through the Lens of the Hessian Z. Yao A. Gholami Kurt Keutzer Michael W. Mahoney ODL 24 292 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 36 215 0 04 Dec 2019
Federated Learning with Personalization Layers Manoj Ghuhan Arivazhagan V. Aggarwal Aaditya Kumar Singh Sunav Choudhary FedML 44 822 0 02 Dec 2019
Scalable Extreme Deconvolution James A. Ritchie Iain Murray 18 1 0 26 Nov 2019