Importance Sampling for Minibatches

6 February 2016

Papers citing "Importance Sampling for Minibatches"

22 / 22 papers shown

Title
A Negative Result on Gradient Matching for Selective Backprop Lukas Balles Cédric Archambeau Giovanni Zappella 37 0 0 08 Dec 2023
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling Jianxin Wei Ergute Bao X. Xiao Yifan Yang 46 20 0 18 Oct 2022
Stop Wasting My Time! Saving Days of ImageNet and BERT Training with Latest Weight Averaging Jean Kaddour MoMe 3DH 24 39 0 29 Sep 2022
Tricks and Plugins to GBM on Images and Sequences Biyi Fang J. Utke Diego Klabjan 25 0 0 01 Mar 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 34 10 0 28 Dec 2021
Adaptive Importance Sampling meets Mirror Descent: a Bias-variance tradeoff Anna Korba Franccois Portier 26 12 0 29 Oct 2021
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss Mingyang Yi Lu Hou Lifeng Shang Xin Jiang Qun Liu Zhi-Ming Ma 12 19 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 38 17 0 02 Mar 2021
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 34 0 0 26 Aug 2020
Adaptive Task Sampling for Meta-Learning Chenghao Liu Zhihao Wang Doyen Sahoo Yuan Fang Kun Zhang Guosheng Lin 25 54 0 17 Jul 2020
Sampling and Update Frequencies in Proximal Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 27 4 0 13 Feb 2020
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 27 30 0 22 Oct 2019
Empirical study towards understanding line search approximations for training neural networks Younghwan Chae D. Wilke 27 11 0 15 Sep 2019
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it Konstantin Mishchenko Filip Hanzely Peter Richtárik 16 13 0 27 Jan 2019
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks Patrick Schwab Lorenz Linhardt W. Karlen CML BDL 13 111 0 01 Oct 2018
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 38 684 0 15 Nov 2017
Safe Adaptive Importance Sampling Sebastian U. Stich Anant Raj Martin Jaggi 27 54 0 07 Nov 2017
Determinantal Point Processes for Mini-Batch Diversification Cheng Zhang Hedvig Kjellström Stephan Mandt 19 35 0 01 May 2017
Coupling Adaptive Batch Sizes with Learning Rates Lukas Balles Javier Romero Philipp Hennig ODL 21 110 0 15 Dec 2016
Big Batch SGD: Automated Inference using Adaptive Batch Sizes Soham De A. Yadav David Jacobs Tom Goldstein ODL 25 62 0 18 Oct 2016
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 93 737 0 19 Mar 2014
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 79 317 0 18 Feb 2014