v1v2v3 (latest)

Optimization Methods for Large-Scale Machine Learning

15 June 2016

Papers citing "Optimization Methods for Large-Scale Machine Learning"

50 / 866 papers shown

Title
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer Vinayak Gupta Srikanta J. Bedathur 92 19 0 16 Jan 2022
Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach T. Wan L. Hong 26 11 0 15 Jan 2022
Federated Optimization of Smooth Loss Functions Ali Jadbabaie A. Makur Devavrat Shah FedML 438 7 0 06 Jan 2022
$Asymptotics of $\ell_2$ Regularized Network Embeddings$ Asymptotics of $\ell_2$ Regularized Network Embeddings A. Davison 96 0 0 05 Jan 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 162 11 0 28 Dec 2021
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations Keju Tang Xiaoliang Wan Chao Yang 77 116 0 28 Dec 2021
Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM) Tengchan Zeng Omid Semiari Walid Saad M. Bennis 65 3 0 26 Dec 2021
Improving Robustness with Image Filtering M. Terzi Mattia Carletti Gian Antonio Susto AAML 60 0 0 21 Dec 2021
Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats Brian Chmiel Ron Banner Elad Hoffer Hilla Ben Yaacov Daniel Soudry MQ 74 24 0 19 Dec 2021
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey Jiaoyan Chen Yuxia Geng Zhuo Chen Jeff Z. Pan Yuan He Wen Zhang Ian Horrocks Hua-zeng Chen 134 49 0 18 Dec 2021
Minimization of Stochastic First-order Oracle Complexity of Adaptive Methods for Nonconvex Optimization Hideaki Iiduka 43 0 0 14 Dec 2021
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions Martin Hutzenthaler Arnulf Jentzen Katharina Pohl Adrian Riekert Luca Scarpa MLT 114 7 0 13 Dec 2021
A Novel Sequential Coreset Method for Gradient Descent Algorithms Jiawei Huang Ru Huang Wenjie Liu N. Freris Huihua Ding 95 16 0 05 Dec 2021
Regularized Newton Method with Global $O(1/k^2)$ Convergence Konstantin Mishchenko 87 41 0 03 Dec 2021
On Large Batch Training and Sharp Minima: A Fokker-Planck Perspective Xiaowu Dai Yuhua Zhu 49 4 0 02 Dec 2021
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 78 5 0 01 Dec 2021
An Optimization Framework for Federated Edge Learning Yangchen Li Ying Cui Vincent K. N. Lau FedML 56 7 0 26 Nov 2021
Random-reshuffled SARAH does not need a full gradient computations Aleksandr Beznosikov Martin Takáč 74 8 0 26 Nov 2021
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule Miao Zhang Jilin Hu Steven W. Su Shirui Pan Xiaojun Chang B. Yang Gholamreza Haffari OOD 115 15 0 25 Nov 2021
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning Siladittya Manna Umapada Pal Saumik Bhattacharya SSL 123 1 0 24 Nov 2021
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning Zhenhuan Yang Yunwen Lei Puyu Wang Tianbao Yang Yiming Ying 80 26 0 23 Nov 2021
Variance Reduction in Deep Learning: More Momentum is All You Need Lionel Tondji S. Kashubin Moustapha Cissé ODL 42 1 0 23 Nov 2021
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits Hao Chen Lili Zheng Raed Al Kontar Garvesh Raskutti 86 3 0 19 Nov 2021
Stationary Behavior of Constant Stepsize SGD Type Algorithms: An Asymptotic Characterization Zaiwei Chen Shancong Mou S. T. Maguluri 55 13 0 11 Nov 2021
AGGLIO: Global Optimization for Locally Convex Functions Debojyoti Dey B. Mukhoty Purushottam Kar 62 2 0 06 Nov 2021
Large-Scale Deep Learning Optimizations: A Comprehensive Survey Xiaoxin He Fuzhao Xue Xiaozhe Ren Yang You 90 15 0 01 Nov 2021
Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow Haitz Sáez de Ocáriz Borde David Sondak P. Protopapas AI4CE 47 3 0 30 Oct 2021
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima Guangyuan Shi Jiaxin Chen Wenlong Zhang Li-Ming Zhan Xiao-Ming Wu CLL 181 160 0 30 Oct 2021
Dynamic Differential-Privacy Preserving SGD Jian Du Song Li Xiangyi Chen Siheng Chen Mingyi Hong 92 33 0 30 Oct 2021
Efficient Meta Subspace Optimization Yoni Choukroun Michael Katz 85 1 0 28 Oct 2021
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent Sharan Vaswani Benjamin Dubois-Taine Reza Babanezhad 98 13 0 21 Oct 2021
Boosting Resource-Constrained Federated Learning Systems with Guessed Updates Mohamed Yassine Boukhari Akash Dhasade Anne-Marie Kermarrec Rafael Pires Othmane Safsafi Rishi Sharma FedML 91 0 0 21 Oct 2021
A Data-Centric Optimization Framework for Machine Learning Oliver Rausch Tal Ben-Nun Nikoli Dryden Andrei Ivanov Shigang Li Torsten Hoefler AI4CE 57 16 0 20 Oct 2021
Optimal randomized classification trees R. Blanquero E. Carrizosa Antonios Tsourdos Dolores Romero Morales 228 47 0 19 Oct 2021
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining Tim Kaler Nickolas Stathas Anne Ouyang A. Iliopoulos Tao B. Schardl C. E. Leiserson Jie Chen GNN 142 55 0 16 Oct 2021
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis Yi Liu Yuanshao Zhu James Jianqiao Yu FedML 74 28 0 14 Oct 2021
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers Yujing Ma Florin Rusu Kesheng Wu A. Sim 102 3 0 13 Oct 2021
Convergence of Random Reshuffling Under The Kurdyka-Łojasiewicz Inequality Xiao Li Andre Milzarek Junwen Qiu 95 20 0 10 Oct 2021
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect Yuqing Wang Minshuo Chen T. Zhao Molei Tao AI4CE 142 42 0 07 Oct 2021
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications Ziqiao Wang Yongyi Mao FedML MLT 124 26 0 07 Oct 2021
Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problems Tommaso Giovannelli G. Kent Luis Nunes Vicente 125 12 0 01 Oct 2021
An Accelerated Stochastic Gradient for Canonical Polyadic Decomposition Ioanna Siaminou A. Liavas 63 4 0 28 Sep 2021
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization Raghu Bollapragada Stefan M. Wild 76 12 0 24 Sep 2021
Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming Sen Na M. Anitescu Mladen Kolar 77 35 0 23 Sep 2021
AdaLoss: A computationally-efficient and provably convergent adaptive gradient method Xiaoxia Wu Yuege Xie S. Du Rachel A. Ward ODL 49 7 0 17 Sep 2021
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods Xin Guo Anran Hu Junzi Zhang OffRL 86 6 0 13 Sep 2021
Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering Jian Xu Shao-Lun Huang Linqi Song Tian-Shing Lan FedML AAML 85 48 0 13 Sep 2021
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information Majid Jahani S. Rusakov Zheng Shi Peter Richtárik Michael W. Mahoney Martin Takávc ODL 51 27 0 11 Sep 2021
Self-adaptive deep neural network: Numerical approximation to functions and PDEs Zhiqiang Cai Jingshuang Chen Min Liu ODL 49 14 0 07 Sep 2021
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation Linda S. L. Tan 108 13 0 01 Sep 2021