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
Stochastic Gradient Descent without Full Data Shuffle Lijie Xu Shuang Qiu Binhang Yuan Jiawei Jiang Cédric Renggli ... Guoliang Li Ji Liu Wentao Wu Jieping Ye Ce Zhang 17 2 0 12 Jun 2022
Fisher SAM: Information Geometry and Sharpness Aware Minimisation Minyoung Kim Da Li S. Hu Timothy M. Hospedales AAML 30 70 0 10 Jun 2022
A Unified Convergence Theorem for Stochastic Optimization Methods Xiao Li Andre Milzarek 41 11 0 08 Jun 2022
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks Zhibin Wang Yong Zhou Yuanming Shi W. Zhuang 43 69 0 06 Jun 2022
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning Kushal Chakrabarti Nikhil Chopra ODL AI4CE 13 6 0 04 Jun 2022
Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees Jue Wang Binhang Yuan Luka Rimanic Yongjun He Tri Dao Beidi Chen Christopher Ré Ce Zhang AI4CE 31 11 0 02 Jun 2022
Computing the Variance of Shuffling Stochastic Gradient Algorithms via Power Spectral Density Analysis Carles Domingo-Enrich 11 0 0 01 Jun 2022
Special Properties of Gradient Descent with Large Learning Rates Amirkeivan Mohtashami Martin Jaggi Sebastian U. Stich MLT 21 9 0 30 May 2022
Standalone Neural ODEs with Sensitivity Analysis Rym Jaroudi Lukáš Malý Gabriel Eilertsen B. Johansson Jonas Unger George Baravdish 23 0 0 27 May 2022
Transformers from an Optimization Perspective Yongyi Yang Zengfeng Huang David Wipf 48 26 0 27 May 2022
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking Zhenxiao Zhang Zhidong Gao Yuanxiong Guo Yanmin Gong FedML 23 33 0 25 May 2022
Online Deep Equilibrium Learning for Regularization by Denoising Jiaming Liu Xiaojian Xu Weijie Gan Shirin Shoushtari Ulugbek S. Kamilov 47 27 0 25 May 2022
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization Benjamin Dubois-Taine Francis R. Bach Quentin Berthet Adrien B. Taylor 30 5 0 25 May 2022
Learning from time-dependent streaming data with online stochastic algorithms Antoine Godichon-Baggioni Nicklas Werge Olivier Wintenberger 40 3 0 25 May 2022
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums Sadegh Farhadkhani R. Guerraoui Nirupam Gupta Rafael Pinot John Stephan FedML 36 67 0 24 May 2022
Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent S. H. Lim Yijun Wan Umut cSimcsekli 40 12 0 23 May 2022
Nothing makes sense in deep learning, except in the light of evolution Artem Kaznatcheev Konrad Paul Kording 11 1 0 20 May 2022
Service Delay Minimization for Federated Learning over Mobile Devices Rui Chen Dian Shi Xiaoqi Qin Dongjie Liu Miao Pan Shuguang Cui FedML 45 32 0 19 May 2022
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning M. Yousefi Angeles Martinez ODL 18 1 0 18 May 2022
Can We Do Better Than Random Start? The Power of Data Outsourcing Yi Chen Jing-rong Dong Xin T. Tong 14 0 0 17 May 2022
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 32 4 0 15 May 2022
Decentralized Stochastic Optimization with Inherent Privacy Protection Yongqiang Wang H. Vincent Poor 29 37 0 08 May 2022
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks Zeyi Tao Jindi Wu Qi Xia Qun Li 36 9 0 05 May 2022
Byzantine Fault Tolerance in Distributed Machine Learning : a Survey Djamila Bouhata Hamouma Moumen Moumen Hamouma Ahcène Bounceur AI4CE 31 7 0 05 May 2022
Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness Dávid Szeghy Mahmoud Aslan Á. Fóthi Balázs Mészáros Z. '. Milacski András Lőrincz AAML 24 0 0 05 May 2022
FedShuffle: Recipes for Better Use of Local Work in Federated Learning Samuel Horváth Maziar Sanjabi Lin Xiao Peter Richtárik Michael G. Rabbat FedML 33 21 0 27 Apr 2022
Streaming Algorithms for High-Dimensional Robust Statistics Ilias Diakonikolas D. Kane Ankit Pensia Thanasis Pittas 24 21 0 26 Apr 2022
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence Sen Na Michal Derezinski Michael W. Mahoney 27 16 0 20 Apr 2022
FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning Afsaneh Mahmoudi H. S. Ghadikolaei José Hélio da Cruz Júnior Carlo Fischione 30 9 0 16 Apr 2022
Minimizing Control for Credit Assignment with Strong Feedback Alexander Meulemans Matilde Tristany Farinha Maria R. Cervera João Sacramento Benjamin Grewe 22 17 0 14 Apr 2022
Rethinking Exponential Averaging of the Fisher C. Puiu 28 1 0 10 Apr 2022
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data B. L. Bars A. Bellet Marc Tommasi Erick Lavoie Anne-Marie Kermarrec FedML 37 25 0 09 Apr 2022
Distributed Evolution Strategies for Black-box Stochastic Optimization Xiaoyu He Zibin Zheng Chuan Chen Yuren Zhou Chuan Luo Qingwei Lin 29 5 0 09 Apr 2022
Federated Learning with Partial Model Personalization Krishna Pillutla Kshitiz Malik Abdel-rahman Mohamed Michael G. Rabbat Maziar Sanjabi Lin Xiao FedML 41 157 0 08 Apr 2022
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency Zhiwu Qing Shiwei Zhang Ziyuan Huang Yi Tian Xu Xiang Wang Mingqian Tang Changxin Gao Rong Jin Nong Sang SSL AI4TS 31 17 0 06 Apr 2022
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise D. Jakovetić Dragana Bajović Anit Kumar Sahu S. Kar Nemanja Milošević Dusan Stamenkovic 19 12 0 06 Apr 2022
Imaging Conductivity from Current Density Magnitude using Neural Networks Bangti Jin Xiyao Li Xiliang Lu 24 12 0 05 Apr 2022
A Derivation of Nesterov's Accelerated Gradient Algorithm from Optimal Control Theory I. Michael Ross 23 4 0 29 Mar 2022
Local optimisation of Nyström samples through stochastic gradient descent Matthew Hutchings B. Gauthier 18 2 0 24 Mar 2022
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography J. Kim Taha Toghani César A. Uribe Anastasios Kyrillidis 35 3 0 22 Mar 2022
A Note on Target Q-learning For Solving Finite MDPs with A Generative Oracle Ziniu Li Tian Xu Yang Yu 55 5 0 22 Mar 2022
Minimum Variance Unbiased N:M Sparsity for the Neural Gradients Brian Chmiel Itay Hubara Ron Banner Daniel Soudry 23 10 0 21 Mar 2022
A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization With Non-isolated Local Minima Tae-Eon Ko Xiantao Li 30 2 0 21 Mar 2022
Learning latent causal relationships in multiple time series J. Dmochowski CML AI4TS 11 0 0 21 Mar 2022
Convergence rates of the stochastic alternating algorithm for bi-objective optimization Suyun Liu Luis Nunes Vicente 31 3 0 20 Mar 2022
On the Properties of Adversarially-Trained CNNs Mattia Carletti M. Terzi Gian Antonio Susto AAML 34 1 0 17 Mar 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Pranay Sharma Rohan Panda Gauri Joshi P. Varshney FedML 21 47 0 09 Mar 2022
Machine Learning based Optimal Feedback Control for Microgrid Stabilization T. Xia Kai Sun W. Kang 14 2 0 09 Mar 2022
A Trainable Approach to Zero-delay Smoothing Spline Interpolation Emilio Ruiz-Moreno Luis Miguel Lopez Ramos B. Beferull-Lozano 11 4 0 07 Mar 2022
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime Difan Zou Jingfeng Wu Vladimir Braverman Quanquan Gu Sham Kakade 19 5 0 07 Mar 2022