Optimization Methods for Large-Scale Machine Learning

15 June 2016

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

50 / 1,407 papers shown

Title
Linear Regression with Distributed Learning: A Generalization Error Perspective Martin Hellkvist Ayça Özçelikkale Anders Ahlén FedML 14 10 0 22 Jan 2021
Clairvoyant Prefetching for Distributed Machine Learning I/O Nikoli Dryden Roman Böhringer Tal Ben-Nun Torsten Hoefler 36 57 0 21 Jan 2021
Learning DNN networks using un-rectifying ReLU with compressed sensing application W. Hwang Shih-Shuo Tung 16 2 0 18 Jan 2021
Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration Congliang Chen Li Shen Fangyu Zou Wei Liu 46 29 0 14 Jan 2021
Machine learning classification of non-Markovian noise disturbing quantum dynamics Stefano Martina S. Gherardini Filippo Caruso 14 8 0 08 Jan 2021
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications Xiang Li Zhihua Zhang 30 4 0 05 Jan 2021
Advances in Electron Microscopy with Deep Learning Jeffrey M. Ede 42 2 0 04 Jan 2021
First-Order Methods for Convex Optimization Pavel Dvurechensky Mathias Staudigl Shimrit Shtern ODL 36 25 0 04 Jan 2021
An iterative K-FAC algorithm for Deep Learning Yingshi Chen ODL 22 1 0 01 Jan 2021
CADA: Communication-Adaptive Distributed Adam Tianyi Chen Ziye Guo Yuejiao Sun W. Yin ODL 14 24 0 31 Dec 2020
Unbiased Gradient Estimation for Distributionally Robust Learning Soumyadip Ghosh M. Squillante OOD 26 7 0 22 Dec 2020
Regularization in network optimization via trimmed stochastic gradient descent with noisy label Kensuke Nakamura Bong-Soo Sohn Kyoung-Jae Won Byung-Woo Hong NoLa 17 0 0 21 Dec 2020
Image-Based Jet Analysis Michael Kagan 30 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 21 13 0 15 Dec 2020
Better scalability under potentially heavy-tailed feedback Matthew J. Holland 28 1 0 14 Dec 2020
Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors Kungang Zhang A. Bui D. Apley 14 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 24 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 38 77 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 12 6 0 10 Dec 2020
Asymptotic study of stochastic adaptive algorithm in non-convex landscape S. Gadat Ioana Gavra 21 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 26 15 0 10 Dec 2020
Adaptive Sequential SAA for Solving Two-stage Stochastic Linear Programs R. Pasupathy Yongjia Song 13 1 0 07 Dec 2020
Block majorization-minimization with diminishing radius for constrained nonconvex optimization Hanbaek Lyu Yuchen Li 23 10 0 07 Dec 2020
When Do Curricula Work? Xiaoxia Wu Ethan Dyer Behnam Neyshabur 33 114 0 05 Dec 2020
Characterization of Excess Risk for Locally Strongly Convex Population Risk Mingyang Yi Ruoyu Wang Zhi-Ming Ma 22 2 0 04 Dec 2020
Learning with risks based on M-location Matthew J. Holland 16 10 0 04 Dec 2020
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum Bao Wang Qiang Ye ODL 49 14 0 03 Dec 2020
Accumulated Decoupled Learning: Mitigating Gradient Staleness in Inter-Layer Model Parallelization Huiping Zhuang Zhiping Lin Kar-Ann Toh 42 4 0 03 Dec 2020
$Convergence of Gradient Algorithms for Nonconvex C^{1+alpha} Cost Functions$ Convergence of Gradient Algorithms for Nonconvex C^{1+alpha} Cost Functions Zixuan Wang Shanjian Tang 18 0 0 01 Dec 2020
A Hypergradient Approach to Robust Regression without Correspondence Yujia Xie Yongyi Mao Simiao Zuo Hongteng Xu X. Ye T. Zhao H. Zha 28 15 0 30 Nov 2020
Is Support Set Diversity Necessary for Meta-Learning? Amrith Rajagopal Setlur Oscar Li Virginia Smith 37 16 0 28 Nov 2020
Sequential convergence of AdaGrad algorithm for smooth convex optimization Cheik Traoré Edouard Pauwels 16 21 0 24 Nov 2020
SMG: A Shuffling Gradient-Based Method with Momentum Trang H. Tran Lam M. Nguyen Quoc Tran-Dinh 25 21 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 36 38 0 23 Nov 2020
Continuous-Time Convergence Rates in Potential and Monotone Games Bolin Gao Lacra Pavel 8 8 0 21 Nov 2020
Convergence Analysis of Homotopy-SGD for non-convex optimization Matilde Gargiani Andrea Zanelli Quoc Tran-Dinh Moritz Diehl Frank Hutter 6 3 0 20 Nov 2020
On the asymptotic rate of convergence of Stochastic Newton algorithms and their Weighted Averaged versions Claire Boyer Antoine Godichon-Baggioni 6 18 0 19 Nov 2020
MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks Tao Huang Yihan Zhang Jiajing Wu Junyuan Fang Zibin Zheng GNN 19 2 0 17 Nov 2020
Policy design in experiments with unknown interference Davide Viviano Jess Rudder 32 7 0 16 Nov 2020
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning Kushal Chakrabarti Nirupam Gupta Nikhil Chopra 58 2 0 15 Nov 2020
Sparse Representations of Positive Functions via First and Second-Order Pseudo-Mirror Descent A. Chakraborty K. Rajawat Alec Koppel 25 3 0 13 Nov 2020
Convergence Properties of Stochastic Hypergradients Riccardo Grazzi Massimiliano Pontil Saverio Salzo 37 26 0 13 Nov 2020
SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization Xubo Yue Maher Nouiehed Raed Al Kontar ODL 22 4 0 10 Nov 2020
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering Ricky T. Q. Chen Dami Choi Lukas Balles David Duvenaud Philipp Hennig ODL 49 6 0 09 Nov 2020
Stochastic Approximation for High-frequency Observations in Data Assimilation Shushu Zhang V. Patel 30 1 0 05 Nov 2020
Gradient-Based Empirical Risk Minimization using Local Polynomial Regression Ali Jadbabaie A. Makur Devavrat Shah 35 6 0 04 Nov 2020
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow Youssef Mroueh Truyen V. Nguyen 31 25 0 04 Nov 2020
Quantized Variational Inference Amir Dib 17 1 0 04 Nov 2020
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance Thinh T. Doan 19 45 0 03 Nov 2020
Asynchronous Parallel Stochastic Quasi-Newton Methods Qianqian Tong Guannan Liang Xingyu Cai Chunjiang Zhu J. Bi ODL 32 9 0 02 Nov 2020