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
Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks Yunwen Lei Rong Jin Yiming Ying MLT 40 18 0 19 Sep 2022
On the Soft-Subnetwork for Few-shot Class Incremental Learning Haeyong Kang Jaehong Yoon Sultan Rizky Hikmawan Madjid Sung Ju Hwang Chang D. Yoo CLL 52 41 0 15 Sep 2022
Fast online ranking with fairness of exposure Nicolas Usunier Virginie Do Elvis Dohmatob 32 18 0 13 Sep 2022
Convergence of Batch Updating Methods with Approximate Gradients and/or Noisy Measurements: Theory and Computational Results Tadipatri Uday M. Vidyasagar 28 0 0 12 Sep 2022
Model-free Subsampling Method Based on Uniform Designs Mei Zhang Yongdao Zhou Zhengze Zhou Aijun Zhang 11 14 0 08 Sep 2022
Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz Method Tianhao Hu Bangti Jin Zhi Zhou 31 6 0 07 Sep 2022
Cooperative coevolutionary Modified Differential Evolution with Distance-based Selection for Large-Scale Optimization Problems in noisy environments through an automatic Random Grouping Rui Zhong M. Munetomo 18 0 0 02 Sep 2022
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning Kazusato Oko Shunta Akiyama Tomoya Murata Taiji Suzuki FedML 41 0 0 01 Sep 2022
Flexible Vertical Federated Learning with Heterogeneous Parties Timothy Castiglia Shiqiang Wang S. Patterson FedML 42 34 0 26 Aug 2022
A simplified convergence theory for Byzantine resilient stochastic gradient descent Lindon Roberts E. Smyth 31 3 0 25 Aug 2022
A Graphical Model for Fusing Diverse Microbiome Data Mehmet Aktukmak Haonan Zhu M. Chevrette Julia Nepper S. Magesh J. Handelsman Alfred Hero 24 2 0 21 Aug 2022
Pandemic Control, Game Theory and Machine Learning Yao Xuan R. Balkin Jiequn Han Ruimeng Hu Héctor D. Ceniceros AI4CE 16 1 0 18 Aug 2022
Detection and Mitigation of Byzantine Attacks in Distributed Training Konstantinos Konstantinidis Namrata Vaswani Aditya Ramamoorthy AAML 34 0 0 17 Aug 2022
Exponential Concentration in Stochastic Approximation K. Law N. Walton Shan Yang 21 0 0 15 Aug 2022
Adaptive Learning Rates for Faster Stochastic Gradient Methods Samuel Horváth Konstantin Mishchenko Peter Richtárik ODL 41 7 0 10 Aug 2022
Quantization enabled Privacy Protection in Decentralized Stochastic Optimization Yongqiang Wang Tamer Basar 32 44 0 07 Aug 2022
Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions Sheheryar Mehmood Peter Ochs 37 3 0 05 Aug 2022
Adaptive Stochastic Gradient Descent for Fast and Communication-Efficient Distributed Learning Serge Kas Hanna Rawad Bitar Parimal Parag Venkateswara Dasari S. E. Rouayheb 30 2 0 04 Aug 2022
SGEM: stochastic gradient with energy and momentum Hailiang Liu Xuping Tian 17 4 0 03 Aug 2022
An adjoint-free algorithm for conditional nonlinear optimal perturbations (CNOPs) via sampling Bin Shi Guodong Sun 15 5 0 01 Aug 2022
Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability Z. Li Zixuan Wang Jian Li 19 44 0 26 Jul 2022
Approximation Power of Deep Neural Networks: an explanatory mathematical survey Mohammad Motamed 12 3 0 19 Jul 2022
Is Integer Arithmetic Enough for Deep Learning Training? Alireza Ghaffari Marzieh S. Tahaei Mohammadreza Tayaranian M. Asgharian V. Nia MQ 16 16 0 18 Jul 2022
On uniform-in-time diffusion approximation for stochastic gradient descent Lei Li Yuliang Wang 48 3 0 11 Jul 2022
Deep neural network based adaptive learning for switched systems Junjie He Zhihang Xu Qifeng Liao AI4CE 10 2 0 11 Jul 2022
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation Alain Durmus Eric Moulines A. Naumov S. Samsonov 31 24 0 10 Jul 2022
Scalable K-FAC Training for Deep Neural Networks with Distributed Preconditioning Lin Zhang S. Shi Wei Wang Bo-wen Li 38 10 0 30 Jun 2022
Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction Tianwei Cao Qianqian Xu Zhiyong Yang Qingming Huang 36 7 0 28 Jun 2022
Supervised Learning with General Risk Functionals Liu Leqi Audrey Huang Zachary Chase Lipton Kamyar Azizzadenesheli 25 5 0 27 Jun 2022
$On bounds for norms of reparameterized ReLU artificial neural network parameters: sums of fractional powers of the Lipschitz norm control the network parameter vector$ On bounds for norms of reparameterized ReLU artificial neural network parameters: sums of fractional powers of the Lipschitz norm control the network parameter vector Arnulf Jentzen T. Kröger 30 0 0 27 Jun 2022
Theoretical analysis of Adam using hyperparameters close to one without Lipschitz smoothness Hideaki Iiduka 25 5 0 27 Jun 2022
Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert Yoonhyung Lee Sungdong Lee Joong-Ho Won FedML OffRL 23 3 0 25 Jun 2022
Stochastic Langevin Differential Inclusions with Applications to Machine Learning F. Difonzo Vyacheslav Kungurtsev Jakub Mareˇcek 26 2 0 23 Jun 2022
GACT: Activation Compressed Training for Generic Network Architectures Xiaoxuan Liu Lianmin Zheng Dequan Wang Yukuo Cen Weize Chen ... Zhiyuan Liu Jie Tang Joey Gonzalez Michael W. Mahoney Alvin Cheung VLM GNN MQ 25 30 0 22 Jun 2022
On the Maximum Hessian Eigenvalue and Generalization Simran Kaur Jérémy E. Cohen Zachary Chase Lipton 26 41 0 21 Jun 2022
A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequences Han Shen Tianyi Chen 52 15 0 21 Jun 2022
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates Yann Fraboni Richard Vidal Laetitia Kameni Marco Lorenzi FedML 27 24 0 21 Jun 2022
Finding Optimal Policy for Queueing Models: New Parameterization Trang H. Tran Lam M. Nguyen K. Scheinberg OffRL 28 2 0 21 Jun 2022
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data Timothy Castiglia Anirban Das Shiqiang Wang S. Patterson FedML 27 48 0 16 Jun 2022
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning Anastasia Koloskova Sebastian U. Stich Martin Jaggi FedML 30 79 0 16 Jun 2022
Gradient Descent for Low-Rank Functions Romain Cosson Ali Jadbabaie A. Makur Amirhossein Reisizadeh Devavrat Shah 33 3 0 16 Jun 2022
Adaptive Expert Models for Personalization in Federated Learning Martin Isaksson Edvin Listo Zec R. Coster D. Gillblad vSarunas Girdzijauskas FedML 22 5 0 15 Jun 2022
On the fast convergence of minibatch heavy ball momentum Raghu Bollapragada Tyler Chen Rachel A. Ward 34 17 0 15 Jun 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu Wang Sheng Zha George Karypis 27 69 0 14 Jun 2022
Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems Yunwen Lei 11 16 0 14 Jun 2022
A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization Dounia Lakhmiri D. Orban Andrea Lodi 9 0 0 14 Jun 2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Kyurae Kim Jisu Oh Jacob R. Gardner Adji Bousso Dieng Hongseok Kim BDL 37 8 0 13 Jun 2022
Modeling the Machine Learning Multiverse Samuel J. Bell Onno P. Kampman Jesse Dodge Neil D. Lawrence 26 17 0 13 Jun 2022
Anchor Sampling for Federated Learning with Partial Client Participation Feijie Wu Song Guo Zhihao Qu Shiqi He Ziming Liu Jing Gao FedML 36 12 0 13 Jun 2022
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Lam M. Nguyen Trang H. Tran 32 2 0 13 Jun 2022