Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling

13 May 2014

Tong Zhang

Papers citing "Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling"

41 / 41 papers shown

Title
Efficient multi-view training for 3D Gaussian Splatting M. Choi Injae Kim Hyunwoo J. Kim 3DGS 30 0 0 15 Jun 2025
Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL Jiarui Yao Yifan Hao Hanning Zhang Hanze Dong Wei Xiong Nan Jiang Tong Zhang LRM 163 2 0 05 May 2025
Rainfall regression from C-band Synthetic Aperture Radar using Multi-Task Generative Adversarial Networks A. Colin R. Husson 55 0 0 05 Nov 2024
Simulation Model Calibration with Dynamic Stratification and Adaptive Sampling Pranav Jain Sara Shashaani E. Byon 27 1 0 25 Jan 2024
DPP-based Client Selection for Federated Learning with Non-IID Data Yuxuan Zhang Chao Xu Howard H. Yang Xijun Wang Tony Q.S. Quek FedML 87 5 0 30 Mar 2023
Preferential Subsampling for Stochastic Gradient Langevin Dynamics Srshti Putcha Christopher Nemeth Paul Fearnhead 59 0 0 28 Oct 2022
A Survey of Dataset Refinement for Problems in Computer Vision Datasets Zhijing Wan Zhixiang Wang CheukTing Chung Zheng Wang 97 10 0 21 Oct 2022
Parallel and Streaming Wavelet Neural Networks for Classification and Regression under Apache Spark E Venkatesh Yelleti Vivek V. Ravi Shiva Shankar Orsu 63 6 0 07 Sep 2022
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling Alexander Tyurin Lukang Sun Konstantin Burlachenko Peter Richtárik 48 8 0 05 Jun 2022
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks Seyyedali Hosseinalipour Su Wang Nicolò Michelusi Vaneet Aggarwal Christopher G. Brinton David J. Love M. Chiang 99 27 0 07 Feb 2022
Approximate Inference via Clustering Qianqian Song 64 0 0 28 Nov 2021
Uniform Sampling over Episode Difficulty Sébastien M. R. Arnold Guneet Singh Dhillon Avinash Ravichandran Stefano Soatto 65 14 0 03 Aug 2021
Prototypical Graph Contrastive Learning Shuai Lin Pan Zhou Zi-Yuan Hu Shuojia Wang Ruihui Zhao Yefeng Zheng Liang Lin Eric Xing Xiaodan Liang 92 88 0 17 Jun 2021
Drill the Cork of Information Bottleneck by Inputting the Most Important Data Xinyu Peng Jiawei Zhang Feiyue Wang Li Li 31 6 0 15 May 2021
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss Mingyang Yi Lu Hou Lifeng Shang Xin Jiang Qun Liu Zhi-Ming Ma 122 20 0 16 Mar 2021
Variance Reduced Training with Stratified Sampling for Forecasting Models Yucheng Lu Youngsuk Park Lifan Chen Bernie Wang Christopher De Sa Dean Phillips Foster AI4TS 80 17 0 02 Mar 2021
A Study of Gradient Variance in Deep Learning Fartash Faghri David Duvenaud David J. Fleet Jimmy Ba FedML ODL 59 27 0 09 Jul 2020
Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation Beidi Chen Yingchen Xu Anshumali Shrivastava 70 16 0 30 Oct 2019
Distributed Computation for Marginal Likelihood based Model Choice Alexander K. Buchholz Daniel Ahfock S. Richardson FedML 66 5 0 10 Oct 2019
Near Optimal Stratified Sampling Tiancheng Yu Xiyu Zhai S. Sra 37 3 0 26 Jun 2019
Submodular Batch Selection for Training Deep Neural Networks K. J. Joseph R. VamshiTeja Krishnakant Singh V. Balasubramanian 72 23 0 20 Jun 2019
Stochastic Gradients for Large-Scale Tensor Decomposition T. Kolda David Hong 78 56 0 04 Jun 2019
Efficient posterior sampling for high-dimensional imbalanced logistic regression Deborshee Sen Matthias Sachs Jianfeng Lu David B. Dunson 111 13 0 27 May 2019
Online Variance Reduction with Mixtures Zalan Borsos Sebastian Curi Kfir Y. Levy Andreas Krause 43 14 0 29 Mar 2019
Accelerating Minibatch Stochastic Gradient Descent using Typicality Sampling Xinyu Peng Li Li Feiyue Wang BDL 137 59 0 11 Mar 2019
Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation Mina Rezaei Haojin Yang Christoph Meinel GAN SSeg MedIm 92 21 0 09 Oct 2018
Accelerating Stochastic Gradient Descent Using Antithetic Sampling Jingchang Liu Linli Xu 49 2 0 07 Oct 2018
Predictive Collective Variable Discovery with Deep Bayesian Models M. Schöberl N. Zabaras P. Koutsourelakis 66 34 0 18 Sep 2018
The Effect of Network Width on the Performance of Large-batch Training Lingjiao Chen Hongyi Wang Jinman Zhao Dimitris Papailiopoulos Paraschos Koutris 87 22 0 11 Jun 2018
Active Mini-Batch Sampling using Repulsive Point Processes Cheng Zhang Cengiz Öztireli Stephan Mandt G. Salvi 82 36 0 08 Apr 2018
Faster Learning by Reduction of Data Access Time Vinod Kumar Chauhan A. Sharma Kalpana Dahiya 29 5 0 18 Jan 2018
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 233 698 0 15 Nov 2017
A Novel Stochastic Stratified Average Gradient Method: Convergence Rate and Its Complexity Aixiang Chen Bingchuan Chen Xiaolong Chai Rui-Ling Bian Hengguang Li 67 22 0 21 Oct 2017
Stochastic Optimization with Bandit Sampling Farnood Salehi L. E. Celis Patrick Thiran 46 25 0 08 Aug 2017
On Sampling Strategies for Neural Network-based Collaborative Filtering Ting-Li Chen Yizhou Sun Yue Shi Liangjie Hong 70 226 0 23 Jun 2017
Determinantal Point Processes for Mini-Batch Diversification Cheng Zhang Hedvig Kjellström Stephan Mandt 85 35 0 01 May 2017
Learning a Metric Embedding for Face Recognition using the Multibatch Method Oren Tadmor Y. Wexler Tal Rosenwein Shai Shalev-Shwartz Amnon Shashua CVBM 80 53 0 24 May 2016
Importance Sampling for Minibatches Dominik Csiba Peter Richtárik 111 117 0 06 Feb 2016
Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization Yadong Mu Wei Liu Wei Fan 98 33 0 28 Jun 2015
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting Jakub Konecný Jie Liu Peter Richtárik Martin Takáč ODL 146 273 0 16 Apr 2015
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting Jakub Konecný Jie Liu Peter Richtárik Martin Takáč ODL 81 22 0 17 Oct 2014