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
TablEye: Seeing small Tables through the Lens of Images Seungeun Lee Sang-Chul Lee LMTD 21 1 0 04 Jul 2023
Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer Peng Mi Li Shen Tianhe Ren Yiyi Zhou Tianshuo Xu Xiaoshuai Sun Tongliang Liu Rongrong Ji Dacheng Tao AAML 40 2 0 30 Jun 2023
Training Deep Surrogate Models with Large Scale Online Learning Lucas Meyer M. Schouler R. Caulk Alejandro Ribés Bruno Raffin 3DGS AI4CE 27 4 0 28 Jun 2023
G-TRACER: Expected Sharpness Optimization John R. Williams Stephen J. Roberts 35 0 0 24 Jun 2023
Efficient preconditioned stochastic gradient descent for estimation in latent variable models C. Baey Maud Delattre E. Kuhn Jean-Benoist Léger Sarah Lemler 21 4 0 22 Jun 2023
Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models Leonardo Galli Holger Rauhut Mark W. Schmidt 29 11 0 22 Jun 2023
Empirical Risk Minimization with Shuffled SGD: A Primal-Dual Perspective and Improved Bounds Xu Cai Cheuk Yin Lin Jelena Diakonikolas FedML 41 5 0 21 Jun 2023
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates Yuchang Sun Yuyi Mao Jinchao Zhang FedML 28 10 0 21 Jun 2023
Adaptive Federated Learning with Auto-Tuned Clients J. Kim Taha Toghani César A. Uribe Anastasios Kyrillidis FedML 48 6 0 19 Jun 2023
Bootstrapped Representations in Reinforcement Learning Charline Le Lan Stephen Tu Mark Rowland Anna Harutyunyan Rishabh Agarwal Marc G. Bellemare Will Dabney OffRL OOD SSL 77 10 0 16 Jun 2023
Schema-learning and rebinding as mechanisms of in-context learning and emergence Siva K. Swaminathan Antoine Dedieu Rajkumar Vasudeva Raju Murray Shanahan Miguel Lazaro-Gredilla Dileep George 36 9 0 16 Jun 2023
Understanding Optimization of Deep Learning via Jacobian Matrix and Lipschitz Constant Xianbiao Qi Jianan Wang Lei Zhang 18 0 0 15 Jun 2023
Robustly Learning a Single Neuron via Sharpness Puqian Wang Nikos Zarifis Ilias Diakonikolas Jelena Diakonikolas 22 9 0 13 Jun 2023
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems Yangchen Li Ying Cui Vincent K. N. Lau FedML 27 3 0 13 Jun 2023
Analysis of the Relative Entropy Asymmetry in the Regularization of Empirical Risk Minimization Francisco Daunas I. Esnaola S. Perlaza H. Vincent Poor 20 15 0 12 Jun 2023
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates Guojun Xiong Gang Yan Shiqiang Wang Jian Li 18 3 0 11 Jun 2023
Improving Accelerated Federated Learning with Compression and Importance Sampling Michal Grudzieñ Grigory Malinovsky Peter Richtárik FedML 37 9 0 05 Jun 2023
Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning Yao Tang Guangxu Zhu Wei Xu M. H. Cheung T. Lok Shuguang Cui 34 7 0 05 Jun 2023
Decentralized SGD and Average-direction SAM are Asymptotically Equivalent Tongtian Zhu Fengxiang He Kaixuan Chen Mingli Song Dacheng Tao 34 15 0 05 Jun 2023
Toward Understanding Why Adam Converges Faster Than SGD for Transformers Yan Pan Yuanzhi Li 33 41 0 31 May 2023
Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning Junyi Zhu Ruicong Yao Matthew B. Blaschko FedML 8 9 0 31 May 2023
FedDisco: Federated Learning with Discrepancy-Aware Collaboration Rui Ye Mingkai Xu Jianyu Wang Chenxin Xu Siheng Chen Yanfeng Wang FedML 43 61 0 30 May 2023
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality Kejie Tang Weidong Liu Yichen Zhang Xi Chen 21 2 0 28 May 2023
Sharpened Lazy Incremental Quasi-Newton Method Aakash Lahoti Spandan Senapati K. Rajawat Alec Koppel 32 2 0 26 May 2023
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning Yuchang Sun Zehong Lin Yuyi Mao Shi Jin Jinchao Zhang 48 11 0 26 May 2023
XGrad: Boosting Gradient-Based Optimizers With Weight Prediction Lei Guan Dongsheng Li Yanqi Shi Jian Meng ODL 44 2 0 26 May 2023
A Guide Through the Zoo of Biased SGD Yury Demidovich Grigory Malinovsky Igor Sokolov Peter Richtárik 39 23 0 25 May 2023
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method Ahmed Khaled Konstantin Mishchenko Chi Jin ODL 27 23 0 25 May 2023
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization Florian E. Dorner Nikola Konstantinov Georgi Pashaliev Martin Vechev FedML 22 5 0 25 May 2023
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training Yi Shi Yingqi Liu Yan Sun Zihao Lin Li Shen Xueqian Wang Dacheng Tao FedML 45 10 0 24 May 2023
Theoretically Principled Federated Learning for Balancing Privacy and Utility Xiaojin Zhang Wenjie Li Kai Chen Shutao Xia Qian Yang FedML 25 9 0 24 May 2023
DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images Ashish Sinha J. Kawahara Arezou Pakzad Kumar Abhishek Matthieu Ruthven Enjie Ghorbel Anis Kacem Djamila Aouada Ghassan Hamarneh MedIm DiffM 27 3 0 22 May 2023
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods Junchi Yang Xiang Li Ilyas Fatkhullin Niao He 42 15 0 21 May 2023
Stochastic Ratios Tracking Algorithm for Large Scale Machine Learning Problems Shigeng Sun Yuchen Xie 18 3 0 17 May 2023
Online Learning Under A Separable Stochastic Approximation Framework Min Gan Xiang-Xiang Su Guang-yong Chen Jing Chen 28 0 0 12 May 2023
UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization Yiming Jiang Jinlan Liu Dongpo Xu Danilo Mandic 13 4 0 09 May 2023
Over-the-Air Federated Averaging with Limited Power and Privacy Budgets Na Yan Kezhi Wang Cunhua Pan K. K. Chai Feng Shu Jiangzhou Wang FedML 35 2 0 05 May 2023
Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching Tim Kaler A. Iliopoulos P. Murzynowski Tao B. Schardl C. E. Leiserson Jie Chen GNN 24 15 0 04 May 2023
Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty F. Menhorn Gianluca Geraci D. Seidl Youssef M. Marzouk M. Eldred H. Bungartz 18 1 0 04 May 2023
A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification Mehran Pourvahab Seyed Jalaleddin Mousavirad Virginie Felizardo Pedro Gusmão Henriques Zacarias Hamzeh Mohammadigheymasi Nicholas D. Lane Seyed Nooreddin Jafari Nuno M. Garcia 28 2 0 04 May 2023
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees Anastasia Koloskova Hadrien Hendrikx Sebastian U. Stich 112 49 0 02 May 2023
Understanding the Generalization Ability of Deep Learning Algorithms: A Kernelized Renyi's Entropy Perspective Yuxin Dong Tieliang Gong Hao Chen Chen Li 23 4 0 02 May 2023
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy Yi Shi Kang Wei Li Shen Yingqi Liu Xueqian Wang Bo Yuan Dacheng Tao FedML 41 2 0 01 May 2023
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 94 32 0 29 Apr 2023
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems Frank E. Curtis Vyacheslav Kungurtsev Daniel P. Robinson Qi Wang 27 10 0 28 Apr 2023
An Adaptive Policy to Employ Sharpness-Aware Minimization Weisen Jiang Hansi Yang Yu Zhang James T. Kwok AAML 83 31 0 28 Apr 2023
Killing Two Birds with One Stone: Quantization Achieves Privacy in Distributed Learning Guangfeng Yan Tan Li Kui Wu Linqi Song 34 12 0 26 Apr 2023
Model Conversion via Differentially Private Data-Free Distillation Bochao Liu Pengju Wang Shikun Li Dan Zeng Shiming Ge FedML 21 3 0 25 Apr 2023
Optimality of Robust Online Learning Zheng-Chu Guo A. Christmann Lei Shi 29 9 0 20 Apr 2023
Leveraging the two timescale regime to demonstrate convergence of neural networks P. Marion Raphael Berthier 36 5 0 19 Apr 2023