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Stochastic Optimization with Importance Sampling

13 January 2014

Tong Zhang

Papers citing "Stochastic Optimization with Importance Sampling"

50 / 183 papers shown

Title
Carpe Diem, Seize the Samples Uncertain "At the Moment" for Adaptive Batch Selection Hwanjun Song Minseok Kim Sundong Kim Jae-Gil Lee 58 16 0 19 Nov 2019
Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation Beidi Chen Yingchen Xu Anshumali Shrivastava 67 16 0 30 Oct 2019
Minimal Variance Sampling in Stochastic Gradient Boosting Bulat Ibragimov Gleb Gusev 74 25 0 29 Oct 2019
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 70 32 0 22 Oct 2019
Active Learning with Importance Sampling Muni Sreenivas Pydi Vishnu Suresh Lokhande 19 1 0 10 Oct 2019
Straggler-Agnostic and Communication-Efficient Distributed Primal-Dual Algorithm for High-Dimensional Data Mining Zhouyuan Huo Heng-Chiao Huang FedML 47 5 0 09 Oct 2019
Stochastic Optimization for Non-convex Inf-Projection Problems Yan Yan Yi Tian Xu Lijun Zhang Xiaoyu Wang Tianbao Yang 16 3 0 26 Aug 2019
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization Kenji Kawaguchi Haihao Lu ODL 100 64 0 09 Jul 2019
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization Quoc Tran-Dinh Nhan H. Pham T. Dzung Lam M. Nguyen 80 51 0 08 Jul 2019
Submodular Batch Selection for Training Deep Neural Networks K. J. Joseph R. VamshiTeja Krishnakant Singh V. Balasubramanian 72 23 0 20 Jun 2019
ADASS: Adaptive Sample Selection for Training Acceleration Shen-Yi Zhao Hao Gao Wu-Jun Li 50 0 0 11 Jun 2019
Stochastic Gradients for Large-Scale Tensor Decomposition T. Kolda David Hong 74 56 0 04 Jun 2019
One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods Filip Hanzely Peter Richtárik 90 27 0 27 May 2019
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent Eduard A. Gorbunov Filip Hanzely Peter Richtárik 111 147 0 27 May 2019
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks Jiong Zhang Hsiang-Fu Yu Inderjit S. Dhillon 58 27 0 08 May 2019
On Linear Learning with Manycore Processors Eliza Wszola Celestine Mendler-Dünner Martin Jaggi Markus Püschel 63 1 0 02 May 2019
Communication trade-offs for synchronized distributed SGD with large step size Kumar Kshitij Patel Aymeric Dieuleveut FedML 66 27 0 25 Apr 2019
Online Variance Reduction with Mixtures Zalan Borsos Sebastian Curi Kfir Y. Levy Andreas Krause 43 14 0 29 Mar 2019
F10-SGD: Fast Training of Elastic-net Linear Models for Text Classification and Named-entity Recognition Stanislav Peshterliev Alexander Hsieh I. Kiss 20 2 0 27 Feb 2019
Real-time Prediction of Automotive Collision Risk from Monocular Video Derek J. Phillips J. C. Aragon Anjali Roychowdhury Regina Madigan Sunil Chintakindi Mykel J. Kochenderfer 21 15 0 04 Feb 2019
Stochastic Doubly Robust Gradient Kanghoon Lee Jihye Choi Moonsu Cha Jung Kwon Lee Tae-Yoon Kim 25 0 0 21 Dec 2018
An Empirical Study of Example Forgetting during Deep Neural Network Learning Mariya Toneva Alessandro Sordoni Rémi Tachet des Combes Adam Trischler Yoshua Bengio Geoffrey J. Gordon 222 744 0 12 Dec 2018
Stagewise Training Accelerates Convergence of Testing Error Over SGD Zhuoning Yuan Yan Yan Rong Jin Tianbao Yang 107 11 0 10 Dec 2018
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence Yi Tian Xu Qi Qi Qihang Lin Rong Jin Tianbao Yang 87 42 0 28 Nov 2018
Accelerating Stochastic Gradient Descent Using Antithetic Sampling Jingchang Liu Linli Xu 49 2 0 07 Oct 2018
ASVRG: Accelerated Proximal SVRG Fanhua Shang L. Jiao Kaiwen Zhou James Cheng Yan Ren Yufei Jin ODL 96 31 0 07 Oct 2018
Sparsified SGD with Memory Sebastian U. Stich Jean-Baptiste Cordonnier Martin Jaggi 106 753 0 20 Sep 2018
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data Xinlei Pan Sung-Li Chiang John F. Canny 13 1 0 26 Aug 2018
A Fast, Principled Working Set Algorithm for Exploiting Piecewise Linear Structure in Convex Problems Tyler B. Johnson Carlos Guestrin 51 5 0 20 Jul 2018
Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption Martin Bompaire Emmanuel Bacry Stéphane Gaïffas 83 6 0 10 Jul 2018
Asymptotic optimality of adaptive importance sampling B. Delyon François Portier 43 29 0 04 Jun 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 211 1,072 0 24 May 2018
WNGrad: Learn the Learning Rate in Gradient Descent Xiaoxia Wu Rachel A. Ward Léon Bottou 70 87 0 07 Mar 2018
Not All Samples Are Created Equal: Deep Learning with Importance Sampling Angelos Katharopoulos François Fleuret 120 525 0 02 Mar 2018
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning Fanhua Shang Kaiwen Zhou Hongying Liu James Cheng Ivor W. Tsang Lijun Zhang Dacheng Tao L. Jiao 98 67 0 26 Feb 2018
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization Fanhua Shang Yuanyuan Liu Kaiwen Zhou James Cheng K. K. Ng Yuichi Yoshida 94 9 0 26 Feb 2018
Online Variance Reduction for Stochastic Optimization Zalan Borsos Andreas Krause Kfir Y. Levy 50 25 0 13 Feb 2018
Faster Learning by Reduction of Data Access Time Vinod Kumar Chauhan A. Sharma Kalpana Dahiya 29 5 0 18 Jan 2018
Generalization Error Bounds for Noisy, Iterative Algorithms Ankit Pensia Varun Jog Po-Ling Loh 96 113 0 12 Jan 2018
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields Rémi Le Priol Alexandre Piché Simon Lacoste-Julien 73 5 0 22 Dec 2017
Coordinate Descent with Bandit Sampling Farnood Salehi Patrick Thiran L. E. Celis 78 17 0 08 Dec 2017
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 233 698 0 15 Nov 2017
Safe Adaptive Importance Sampling Sebastian U. Stich Anant Raj Martin Jaggi 99 55 0 07 Nov 2017
Accelerate RNN-based Training with Importance Sampling Fei Wang Xiaofeng Gao Guihai Chen Jun Ye 12 0 0 31 Oct 2017
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent Ben London 81 79 0 19 Sep 2017
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems Celestine Mendler-Dünner Thomas Parnell Martin Jaggi FedML 60 0 0 17 Aug 2017
Stochastic Optimization with Bandit Sampling Farnood Salehi L. E. Celis Patrick Thiran 42 25 0 08 Aug 2017
Stochastic, Distributed and Federated Optimization for Machine Learning Jakub Konecný FedML 80 38 0 04 Jul 2017
Approximate Steepest Coordinate Descent Sebastian U. Stich Anant Raj Martin Jaggi 63 16 0 26 Jun 2017
IS-ASGD: Accelerating Asynchronous SGD using Importance Sampling Fei Wang Jun Ye Weichen Li Guihai Chen 78 1 0 26 Jun 2017