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
Truncated Non-Uniform Quantization for Distributed SGD Guangfeng Yan Tan Li Yuanzhang Xiao Congduan Li Linqi Song MQ 17 0 0 02 Feb 2024
Towards Quantum-Safe Federated Learning via Homomorphic Encryption: Learning with Gradients Guangfeng Yan Shanxiang Lyu Hanxu Hou Zhiyong Zheng Linqi Song FedML 8 1 0 02 Feb 2024
HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function M. Akhtar Muhammad Tanveer Mohd. Arshad 18 4 0 30 Jan 2024
Leveraging Nested MLMC for Sequential Neural Posterior Estimation with Intractable Likelihoods Xiliang Yang Yifei Xiong Zhijian He 36 0 0 30 Jan 2024
Low-resolution Prior Equilibrium Network for CT Reconstruction Yijie Yang Qifeng Gao Yuping Duan 32 0 0 28 Jan 2024
Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning Chenyu Zhang Han Wang Aritra Mitra James Anderson 37 18 0 27 Jan 2024
Continuous-time Riemannian SGD and SVRG Flows on Wasserstein Probabilistic Space Mingyang Yi Bohan Wang 32 0 0 24 Jan 2024
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning Yuchang Sun Marios Kountouris Jun Zhang FedML 45 2 0 24 Jan 2024
Fast Nonlinear Two-Time-Scale Stochastic Approximation: Achieving $O(1/k)$ Finite-Sample Complexity Thinh T. Doan 32 7 0 23 Jan 2024
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks Jie Hu Vishwaraj Doshi Do Young Eun 55 2 0 18 Jan 2024
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications Jie Hu Vishwaraj Doshi Do Young Eun 38 4 0 17 Jan 2024
GD doesn't make the cut: Three ways that non-differentiability affects neural network training Siddharth Krishna Kumar AAML 26 2 0 16 Jan 2024
Stochastic optimization with arbitrary recurrent data sampling William G. Powell Hanbaek Lyu 37 0 0 15 Jan 2024
Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy Chengli Tan Jiangshe Zhang Junmin Liu Yicheng Wang Yunda Hao AAML 34 1 0 14 Jan 2024
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization A. F. M. Saif Xiaodong Cui Han Shen Songtao Lu Brian Kingsbury Tianyi Chen 37 3 0 13 Jan 2024
Differential Equations for Continuous-Time Deep Learning Lars Ruthotto AI4TS AI4CE SyDa BDL 37 7 0 08 Jan 2024
A Robbins--Monro Sequence That Can Exploit Prior Information For Faster Convergence Siwei Liu Ke Ma Stephan M. Goetz 19 1 0 06 Jan 2024
On the numerical reliability of nonsmooth autodiff: a MaxPool case study Ryan Boustany 16 0 0 05 Jan 2024
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning Jian Li Yong Liu Wei Wang Haoran Wu Weiping Wang FedML 36 2 0 05 Jan 2024
Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations Serban Stan Mohammad Rostami OOD CLL 36 0 0 02 Jan 2024
SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms Farshed Abdukhakimov Chulu Xiang Dmitry Kamzolov Robert Mansel Gower Martin Takáč 43 2 0 28 Dec 2023
ATE-SG: Alternate Through the Epochs Stochastic Gradient for Multi-Task Neural Networks Stefania Bellavia Francesco Della Santa Alessandra Papini 41 0 0 26 Dec 2023
Parallel Trust-Region Approaches in Neural Network Training: Beyond Traditional Methods Ken Trotti Samuel A. Cruz Alegría Alena Kopanicáková Rolf Krause 26 0 0 21 Dec 2023
Continual Learning: Forget-free Winning Subnetworks for Video Representations Haeyong Kang Jaehong Yoon Sung Ju Hwang Chang D. Yoo CLL 39 2 0 19 Dec 2023
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations Guojun Xiong Gang Yan Shiqiang Wang Jian Li 42 5 0 17 Dec 2023
Physics-Informed Deep Learning of Rate-and-State Fault Friction Cody Rucker Brittany A. Erickson PINN AI4CE 13 5 0 14 Dec 2023
Layered Randomized Quantization for Communication-Efficient and Privacy-Preserving Distributed Learning Guangfeng Yan Tan Li Tian-Shing Lan Kui Wu Linqi Song 19 6 0 12 Dec 2023
An $LDL^T$ Trust-Region Quasi-Newton Method John Brust Philip E. Gill 17 0 0 11 Dec 2023
ELSA: Partial Weight Freezing for Overhead-Free Sparse Network Deployment Paniz Halvachi Alexandra Peste Dan Alistarh Christoph H. Lampert 25 0 0 11 Dec 2023
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation Rui Ye Yaxin Du Zhenyang Ni Siheng Chen Yanfeng Wang FedML 36 5 0 10 Dec 2023
TaskMet: Task-Driven Metric Learning for Model Learning Dishank Bansal Ricky T. Q. Chen Mustafa Mukadam Brandon Amos FedML 28 10 0 08 Dec 2023
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications Rajeeva Laxman Karandikar M. Vidyasagar 25 8 0 05 Dec 2023
A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization Junwen Qiu Xiao Li Andre Milzarek 39 2 0 02 Dec 2023
On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations Antoine Godichon-Baggioni Nicklas Werge ODL 40 3 0 29 Nov 2023
Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent Frederik Köhne Leonie Kreis Anton Schiela Roland A. Herzog 21 1 0 28 Nov 2023
Sensitivity-Based Layer Insertion for Residual and Feedforward Neural Networks Evelyn Herberg Roland A. Herzog Frederik Köhne Leonie Kreis Anton Schiela 14 0 0 27 Nov 2023
Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia Milad Baghalzadeh Shishehgarkhaneh R. Moehler Yihai Fang Amer A. Hijazi Hamed Aboutorab 36 6 0 23 Nov 2023
Soft Random Sampling: A Theoretical and Empirical Analysis Xiaodong Cui Ashish R. Mittal Songtao Lu Wei Zhang G. Saon Brian Kingsbury 48 1 0 21 Nov 2023
Infinite forecast combinations based on Dirichlet process Yinuo Ren Feng Li Yanfei Kang Jue Wang AI4TS 27 0 0 21 Nov 2023
High Probability Guarantees for Random Reshuffling Hengxu Yu Xiao Li 45 2 0 20 Nov 2023
Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization with Optimal Noise Scheduling Naoki Sato Hideaki Iiduka 25 3 0 15 Nov 2023
Non-Uniform Smoothness for Gradient Descent A. Berahas Lindon Roberts Fred Roosta 40 3 0 15 Nov 2023
Robust softmax aggregation on blockchain based federated learning with convergence guarantee Huiyu Wu Diego Klabjan FedML 38 2 0 13 Nov 2023
Differentiable Cutting-plane Layers for Mixed-integer Linear Optimization Gabriele Dragotto Stefan Clarke J. F. Fisac Bartolomeo Stellato 47 5 0 06 Nov 2023
Parameter-Agnostic Optimization under Relaxed Smoothness Florian Hübler Junchi Yang Xiang Li Niao He 34 14 0 06 Nov 2023
Signal Processing Meets SGD: From Momentum to Filter Zhipeng Yao Guisong Chang Jiaqi Zhang Qi Zhang Dazhou Li Yu Zhang ODL 37 0 0 06 Nov 2023
High Probability Convergence of Adam Under Unbounded Gradients and Affine Variance Noise Yusu Hong Junhong Lin 30 8 0 03 Nov 2023
Learning to optimize by multi-gradient for multi-objective optimization Linxi Yang Xinmin Yang L. Tang 18 1 0 01 Nov 2023
Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization Philipp Dahlinger P. Becker Maximilian Hüttenrauch Gerhard Neumann 15 0 0 31 Oct 2023
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise Aleksandar Armacki Pranay Sharma Gauri Joshi Dragana Bajović D. Jakovetić S. Kar 39 5 0 28 Oct 2023