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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
MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an Exponential Convergence Rate Aixiang Chen Chen Jinting Zhang Zanbo Zhang Zhihong Li 60 0 0 21 Feb 2022
L-SVRG and L-Katyusha with Adaptive Sampling Boxin Zhao Boxiang Lyu Mladen Kolar 75 3 0 31 Jan 2022
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao Lingxiao Wang Mladen Kolar Ziqi Liu Qing Cui Jun Zhou Chaochao Chen FedML 152 11 0 28 Dec 2021
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling Bing Luo Wenli Xiao Shiqiang Wang Jianwei Huang Leandros Tassiulas FedML 112 177 0 21 Dec 2021
Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff Anna Korba Franccois Portier 80 14 0 29 Oct 2021
Iterative Teaching by Label Synthesis Weiyang Liu Zhen Liu Hanchen Wang Liam Paull Bernhard Schölkopf Adrian Weller 154 16 0 27 Oct 2021
How Important is Importance Sampling for Deep Budgeted Training? Eric Arazo Diego Ortego Paul Albert Noel E. O'Connor Kevin McGuinness 119 8 0 27 Oct 2021
Meta-learning with an Adaptive Task Scheduler Huaxiu Yao Yu Wang Ying Wei P. Zhao M. Mahdavi Defu Lian Chelsea Finn OOD 84 48 0 26 Oct 2021
Large Batch Experience Replay Thibault Lahire Matthieu Geist Emmanuel Rachelson OffRL 100 13 0 04 Oct 2021
Large Scale Private Learning via Low-rank Reparametrization Da Yu Huishuai Zhang Wei Chen Jian Yin Tie-Yan Liu 76 106 0 17 Jun 2021
Efficient Lottery Ticket Finding: Less Data is More Zhenyu Zhang Xuxi Chen Tianlong Chen Zhangyang Wang 111 54 0 06 Jun 2021
Combining resampling and reweighting for faithful stochastic optimization Jing An Lexing Ying 39 1 0 31 May 2021
SGD with Coordinate Sampling: Theory and Practice Rémi Leluc Franccois Portier 60 6 0 25 May 2021
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning Chaosheng Dong Xiaojie Jin Weihao Gao Yijia Wang Hongyi Zhang Xiang Wu Jianchao Yang Xiaobing Liu 75 5 0 27 Apr 2021
Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders Xinmeng Huang Kun Yuan Xianghui Mao W. Yin 64 13 0 25 Apr 2021
Distributed Learning Systems with First-order Methods Ji Liu Ce Zhang 36 44 0 12 Apr 2021
Stochastic Reweighted Gradient Descent Ayoub El Hanchi D. Stephens 42 8 0 23 Mar 2021
Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes Ayoub El Hanchi D. Stephens 47 13 0 23 Mar 2021
Statistical Measures For Defining Curriculum Scoring Function Vinu Sankar Sadasivan A. Dasgupta 54 2 0 27 Feb 2021
Proximal and Federated Random Reshuffling Konstantin Mishchenko Ahmed Khaled Peter Richtárik FedML 77 32 0 12 Feb 2021
A Comprehensive Study on Optimization Strategies for Gradient Descent In Deep Learning K. Yadav 42 1 0 07 Jan 2021
Quantizing data for distributed learning Osama A. Hanna Yahya H. Ezzeldin Christina Fragouli Suhas Diggavi FedML 101 21 0 14 Dec 2020
Federated Learning under Importance Sampling Elsa Rizk Stefan Vlaski Ali H. Sayed FedML 79 53 0 14 Dec 2020
Optimal Client Sampling for Federated Learning Jiajun He Samuel Horváth Peter Richtárik FedML 89 201 0 26 Oct 2020
Optimal Importance Sampling for Federated Learning Elsa Rizk Stefan Vlaski Ali H. Sayed FedML 82 46 0 26 Oct 2020
Adam with Bandit Sampling for Deep Learning Rui Liu Tianyi Wu Barzan Mozafari 84 24 0 24 Oct 2020
Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding Yongqi Zhang Quanming Yao Lei Chen BDL 43 7 0 24 Oct 2020
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization P. Subramani Nicholas Vadivelu Gautam Kamath 101 83 0 18 Oct 2020
Oort: Efficient Federated Learning via Guided Participant Selection Fan Lai Xiangfeng Zhu H. Madhyastha Mosharaf Chowdhury FedML OODD 133 275 0 12 Oct 2020
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering Jingtao Ding Yuhan Quan Quanming Yao Yong Li Depeng Jin 68 100 0 07 Sep 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 87 0 0 26 Aug 2020
Adaptive Task Sampling for Meta-Learning Chenghao Liu Zhihao Wang Doyen Sahoo Yuan Fang Kun Zhang Guosheng Lin 104 55 0 17 Jul 2020
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay Scott Fujimoto David Meger Doina Precup 96 58 0 12 Jul 2020
AdaScale SGD: A User-Friendly Algorithm for Distributed Training Tyler B. Johnson Pulkit Agrawal Haijie Gu Carlos Guestrin ODL 90 37 0 09 Jul 2020
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks Weilin Cong R. Forsati M. Kandemir M. Mahdavi 101 88 0 24 Jun 2020
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization Ahmed Khaled Othmane Sebbouh Nicolas Loizou Robert Mansel Gower Peter Richtárik 119 47 0 20 Jun 2020
Gradient Descent in RKHS with Importance Labeling Tomoya Murata Taiji Suzuki 51 3 0 19 Jun 2020
Graph Learning with Loss-Guided Training Eliav Buchnik E. Cohen 45 1 0 31 May 2020
Accelerated Convergence for Counterfactual Learning to Rank R. Jagerman Maarten de Rijke BDL OffRL 16 13 0 21 May 2020
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness Jinke Ren Yinghui He Dingzhu Wen Guanding Yu Kaibin Huang Dongning Guo 108 197 0 01 Apr 2020
Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling Setareh Ariafar Zelda E. Mariet Ehsan Elhamifar Dana Brooks Jennifer Dy Jasper Snoek 50 3 0 23 Feb 2020
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients Ruilin Li Xin Wang H. Zha Molei Tao 34 4 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 77 11 0 20 Feb 2020
Sampling and Update Frequencies in Proximal Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 54 4 0 13 Feb 2020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization Samuel Horváth Lihua Lei Peter Richtárik Michael I. Jordan 114 30 0 13 Feb 2020
Federated Learning of a Mixture of Global and Local Models Filip Hanzely Peter Richtárik FedML 90 386 0 10 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 105 187 0 09 Feb 2020
Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach Jialin Dong Jun Zhang Yuanming Shi Jessie Hui Wang 43 23 0 28 Jan 2020
Choosing the Sample with Lowest Loss makes SGD Robust Vatsal Shah Xiaoxia Wu Sujay Sanghavi 65 44 0 10 Jan 2020
A Fast Sampling Gradient Tree Boosting Framework D. Zhou Zhongming Jin Tong Zhang 16 2 0 20 Nov 2019