v1v2v3 (latest)

Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

50 / 867 papers shown

Title
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning Haibo Yang Minghong Fang Jia Liu FedML 105 263 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 91 31 0 22 Jan 2021
Approximate Byzantine Fault-Tolerance in Distributed Optimization Shuo Liu Nirupam Gupta Nitin H. Vaidya 84 43 0 22 Jan 2021
Gravity Optimizer: a Kinematic Approach on Optimization in Deep Learning Dariush Bahrami Sadegh Pouriyan Zadeh ODL 44 5 0 22 Jan 2021
Linear Regression with Distributed Learning: A Generalization Error Perspective Martin Hellkvist Ayça Özçelikkale Anders Ahlén FedML 45 10 0 22 Jan 2021
Clairvoyant Prefetching for Distributed Machine Learning I/O Nikoli Dryden Roman Böhringer Tal Ben-Nun Torsten Hoefler 79 58 0 21 Jan 2021
First-Order Methods for Convex Optimization Pavel Dvurechensky Mathias Staudigl Shimrit Shtern ODL 92 26 0 04 Jan 2021
CADA: Communication-Adaptive Distributed Adam Tianyi Chen Ziye Guo Yuejiao Sun W. Yin ODL 49 24 0 31 Dec 2020
Image-Based Jet Analysis Michael Kagan 73 7 0 17 Dec 2020
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? Antoine Grosnit Alexander I. Cowen-Rivers Rasul Tutunov Ryan-Rhys Griffiths Jun Wang Haitham Bou-Ammar 105 14 0 15 Dec 2020
Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors Kungang Zhang A. Bui D. Apley 48 10 0 12 Dec 2020
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective A. R. Geist Sebastian Trimpe AI4CE 90 17 0 11 Dec 2020
Recent Theoretical Advances in Non-Convex Optimization Marina Danilova Pavel Dvurechensky Alexander Gasnikov Eduard A. Gorbunov Sergey Guminov Dmitry Kamzolov Innokentiy Shibaev 129 79 0 11 Dec 2020
Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation Sanae Lotfi Tiphaine Bonniot de Ruisselet D. Orban Andrea Lodi ODL 26 6 0 10 Dec 2020
Asymptotic study of stochastic adaptive algorithm in non-convex landscape S. Gadat Ioana Gavra 82 18 0 10 Dec 2020
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning Canh T. Dinh N. H. Tran Tuan Dung Nguyen Wei Bao A. R. Balef B. Zhou Albert Y. Zomaya FedML 110 16 0 10 Dec 2020
Block majorization-minimization with diminishing radius for constrained nonconvex optimization Hanbaek Lyu Yuchen Li 70 10 0 07 Dec 2020
When Do Curricula Work? Xiaoxia Wu Ethan Dyer Behnam Neyshabur 96 118 0 05 Dec 2020
Learning with risks based on M-location Matthew J. Holland 67 10 0 04 Dec 2020
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum Bao Wang Qiang Ye ODL 99 14 0 03 Dec 2020
A Hypergradient Approach to Robust Regression without Correspondence Yujia Xie Yongyi Mao Simiao Zuo Hongteng Xu X. Ye T. Zhao H. Zha 107 15 0 30 Nov 2020
Sequential convergence of AdaGrad algorithm for smooth convex optimization Cheik Traoré Edouard Pauwels 51 22 0 24 Nov 2020
SMG: A Shuffling Gradient-Based Method with Momentum Trang H. Tran Lam M. Nguyen Quoc Tran-Dinh 78 22 0 24 Nov 2020
On the Convergence of Continuous Constrained Optimization for Structure Learning Ignavier Ng Sébastien Lachapelle Nan Rosemary Ke Simon Lacoste-Julien Kun Zhang 105 38 0 23 Nov 2020
Continuous-Time Convergence Rates in Potential and Monotone Games Bolin Gao Lacra Pavel 22 8 0 21 Nov 2020
On the asymptotic rate of convergence of Stochastic Newton algorithms and their Weighted Averaged versions Claire Boyer Antoine Godichon-Baggioni 68 19 0 19 Nov 2020
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning Kushal Chakrabarti Nirupam Gupta Nikhil Chopra 74 2 0 15 Nov 2020
Convergence Properties of Stochastic Hypergradients Riccardo Grazzi Massimiliano Pontil Saverio Salzo 112 26 0 13 Nov 2020
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering Ricky T. Q. Chen Dami Choi Lukas Balles David Duvenaud Philipp Hennig ODL 86 6 0 09 Nov 2020
Stochastic Approximation for High-frequency Observations in Data Assimilation Shushu Zhang V. Patel 44 1 0 05 Nov 2020
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow Youssef Mroueh Truyen V. Nguyen 70 25 0 04 Nov 2020
Quantized Variational Inference Amir Dib 50 1 0 04 Nov 2020
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance Thinh T. Doan 99 46 0 03 Nov 2020
Asynchronous Parallel Stochastic Quasi-Newton Methods Qianqian Tong Guannan Liang Xingyu Cai Chunjiang Zhu J. Bi ODL 94 9 0 02 Nov 2020
Adversarial Attacks on Optimization based Planners Sai H. Vemprala Ashish Kapoor AAML 131 12 0 30 Oct 2020
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes Marten van Dijk Nhuong V. Nguyen Toan N. Nguyen Lam M. Nguyen Quoc Tran-Dinh Phuong Ha Nguyen FedML 116 10 0 27 Oct 2020
Optimal Client Sampling for Federated Learning Jiajun He Samuel Horváth Peter Richtárik FedML 93 201 0 26 Oct 2020
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD Jiayi Wang Shiqiang Wang Rong-Rong Chen Mingyue Ji FedML 108 55 0 24 Oct 2020
Sample Efficient Reinforcement Learning with REINFORCE Junzi Zhang Jongho Kim Brendan O'Donoghue Stephen P. Boyd 118 113 0 22 Oct 2020
How Data Augmentation affects Optimization for Linear Regression Boris Hanin Yi Sun 86 16 0 21 Oct 2020
Progressive Batching for Efficient Non-linear Least Squares Huu Le Christopher Zach E. Rosten Oliver J. Woodford 54 3 0 21 Oct 2020
Dual Averaging is Surprisingly Effective for Deep Learning Optimization Samy Jelassi Aaron Defazio 61 5 0 20 Oct 2020
Bi-level Score Matching for Learning Energy-based Latent Variable Models Fan Bao Chongxuan Li Kun Xu Hang Su Jun Zhu Bo Zhang 76 14 0 15 Oct 2020
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers Zheng Chai Yujing Chen Ali Anwar Liang Zhao Yue Cheng Huzefa Rangwala FedML 82 124 0 12 Oct 2020
AEGD: Adaptive Gradient Descent with Energy Hailiang Liu Xuping Tian ODL 55 11 0 10 Oct 2020
A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning Xinyue Liang Alireza M. Javid Mikael Skoglund Saikat Chatterjee FedML 60 4 0 29 Sep 2020
Normalization Techniques in Training DNNs: Methodology, Analysis and Application Lei Huang Jie Qin Yi Zhou Fan Zhu Li Liu Ling Shao AI4CE 176 275 0 27 Sep 2020
An optimization problem for continuous submodular functions L. Csirmaz 69 2 0 26 Sep 2020
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 197 80 0 17 Sep 2020
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets Reuben Dorent Thomas C Booth Wenqi Li Carole H. Sudre S. Kafiabadi M. Jorge Cardoso Sebastien Ourselin Tom Vercauteren 48 25 0 08 Sep 2020