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Optimization Methods for Large-Scale Machine Learning

Optimization Methods for Large-Scale Machine Learning

15 June 2016
Léon Bottou
Frank E. Curtis
J. Nocedal
ArXivPDFHTML

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

50 / 1,406 papers shown
Title
Convergence Conditions for Stochastic Line Search Based Optimization of
  Over-parametrized Models
Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models
Matteo Lapucci
Davide Pucci
37
1
0
06 Aug 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning
Differentially Private Block-wise Gradient Shuffle for Deep Learning
Zilong Zhang
FedML
34
0
0
31 Jul 2024
Adaptive Mix for Semi-Supervised Medical Image Segmentation
Adaptive Mix for Semi-Supervised Medical Image Segmentation
Zhiqiang Shen
Peng Cao
Junming Su
Jinzhu Yang
Osmar R. Zaiane
51
0
0
31 Jul 2024
An Effective Dynamic Gradient Calibration Method for Continual Learning
An Effective Dynamic Gradient Calibration Method for Continual Learning
Weichen Lin
Jiaxiang Chen
Ru Huang
Huihua Ding
CLL
44
0
0
30 Jul 2024
PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning
PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning
M. A. Ma'sum
Mahardhika Pratama
Savitha Ramasamy
Lin Liu
Habibullah Habibullah
Ryszard Kowalczyk
CLL
41
0
0
30 Jul 2024
Learning Random Numbers to Realize Appendable Memory System for
  Artificial Intelligence to Acquire New Knowledge after Deployment
Learning Random Numbers to Realize Appendable Memory System for Artificial Intelligence to Acquire New Knowledge after Deployment
Kazunori D Yamada
21
0
0
29 Jul 2024
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware
  Submodel Extraction
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
Feijie Wu
Xingchen Wang
Yaqing Wang
Tianci Liu
Lu Su
Jing Gao
FedML
51
3
0
28 Jul 2024
Many Perception Tasks are Highly Redundant Functions of their Input Data
Many Perception Tasks are Highly Redundant Functions of their Input Data
Rahul Ramesh
Anthony Bisulco
Ronald W. DiTullio
Linran Wei
Vijay Balasubramanian
Kostas Daniilidis
Pratik Chaudhari
44
2
0
18 Jul 2024
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
Haiquan Lu
Xiaotian Liu
Yefan Zhou
Qunli Li
Kurt Keutzer
Michael W. Mahoney
Yujun Yan
Huanrui Yang
Yaoqing Yang
45
1
0
17 Jul 2024
Enhancing Stochastic Optimization for Statistical Efficiency Using
  ROOT-SGD with Diminishing Stepsize
Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize
Tong Zhang
Chris Junchi Li
38
0
0
15 Jul 2024
Stabilized Proximal-Point Methods for Federated Optimization
Stabilized Proximal-Point Methods for Federated Optimization
Xiaowen Jiang
Anton Rodomanov
Sebastian U. Stich
FedML
46
1
0
09 Jul 2024
An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton
  Stepsizes
An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
Antonio Orvieto
Lin Xiao
42
3
0
05 Jul 2024
Stochastic Differential Equations models for Least-Squares Stochastic
  Gradient Descent
Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
Adrien Schertzer
Loucas Pillaud-Vivien
31
0
0
02 Jul 2024
Deterministic and Stochastic Frank-Wolfe Recursion on Probability Spaces
Deterministic and Stochastic Frank-Wolfe Recursion on Probability Spaces
Di Yu
Shane G. Henderson
R. Pasupathy
15
0
0
29 Jun 2024
Unbiased least squares regression via averaged stochastic gradient
  descent
Unbiased least squares regression via averaged stochastic gradient descent
Nabil Kahalé
37
0
0
26 Jun 2024
Efficient k-means with Individual Fairness via Exponential Tilting
Efficient k-means with Individual Fairness via Exponential Tilting
Shengkun Zhu
Jinshan Zeng
Yuan Sun
Sheng Wang
Xiaodong Li
Zhiyong Peng
52
0
0
24 Jun 2024
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing
  Backpropagation
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
Yuchen Yang
Yingdong Shi
Cheems Wang
Xiantong Zhen
Yuxuan Shi
Jun Xu
40
1
0
24 Jun 2024
Towards Timely Video Analytics Services at the Network Edge
Towards Timely Video Analytics Services at the Network Edge
Xishuo Li
Shan Zhang
Yuejiao Huang
Xiao Ma
Zhiyuan Wang
Hongbin Luo
33
1
0
21 Jun 2024
Information Guided Regularization for Fine-tuning Language Models
Information Guided Regularization for Fine-tuning Language Models
Mandar Sharma
Nikhil Muralidhar
Shengzhe Xu
Raquib Bin Yousuf
Naren Ramakrishnan
38
0
0
20 Jun 2024
Communication-Efficient Adaptive Batch Size Strategies for Distributed
  Local Gradient Methods
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods
Tim Tsz-Kit Lau
Weijian Li
Chenwei Xu
Han Liu
Mladen Kolar
44
1
0
20 Jun 2024
Towards Exact Gradient-based Training on Analog In-memory Computing
Towards Exact Gradient-based Training on Analog In-memory Computing
Zhaoxian Wu
Tayfun Gokmen
Malte J. Rasch
Tianyi Chen
34
2
0
18 Jun 2024
Generative vs. Discriminative modeling under the lens of uncertainty
  quantification
Generative vs. Discriminative modeling under the lens of uncertainty quantification
Elouan Argouarc'h
François Desbouvries
Eric Barat
Eiji Kawasaki
UQCV
46
0
0
13 Jun 2024
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees
A. Banerjee
Qiaobo Li
Yingxue Zhou
52
0
0
11 Jun 2024
A Generalized Version of Chung's Lemma and its Applications
A Generalized Version of Chung's Lemma and its Applications
Li Jiang
Xiao Li
Andre Milzarek
Junwen Qiu
45
1
0
09 Jun 2024
Convergence Analysis of Adaptive Gradient Methods under Refined
  Smoothness and Noise Assumptions
Convergence Analysis of Adaptive Gradient Methods under Refined Smoothness and Noise Assumptions
Devyani Maladkar
Ruichen Jiang
Aryan Mokhtari
43
6
0
07 Jun 2024
Efficient Data-Parallel Continual Learning with Asynchronous Distributed
  Rehearsal Buffers
Efficient Data-Parallel Continual Learning with Asynchronous Distributed Rehearsal Buffers
Thomas Bouvier
Bogdan Nicolae
Hugo Chaugier
Alexandru Costan
Ian Foster
Gabriel Antoniu
44
1
0
05 Jun 2024
Demystifying SGD with Doubly Stochastic Gradients
Demystifying SGD with Doubly Stochastic Gradients
Kyurae Kim
Joohwan Ko
Yian Ma
Jacob R. Gardner
53
0
0
03 Jun 2024
Privacy-Aware Randomized Quantization via Linear Programming
Privacy-Aware Randomized Quantization via Linear Programming
Zhongteng Cai
Xueru Zhang
Mohammad Mahdi Khalili
52
2
0
01 Jun 2024
Enhancing Efficiency of Safe Reinforcement Learning via Sample
  Manipulation
Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation
Shangding Gu
Laixi Shi
Yuhao Ding
Alois Knoll
C. Spanos
Adam Wierman
Ming Jin
OffRL
40
2
0
31 May 2024
Symmetries in Overparametrized Neural Networks: A Mean-Field View
Symmetries in Overparametrized Neural Networks: A Mean-Field View
Javier Maass Martínez
Joaquin Fontbona
FedML
MLT
50
2
0
30 May 2024
A Pontryagin Perspective on Reinforcement Learning
A Pontryagin Perspective on Reinforcement Learning
Onno Eberhard
Claire Vernade
Michael Muehlebach
43
2
0
28 May 2024
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling,
  then Average
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average
Louis Fournier
Adel Nabli
Masih Aminbeidokhti
M. Pedersoli
Eugene Belilovsky
Edouard Oyallon
MoMe
FedML
49
3
0
27 May 2024
Derivatives of Stochastic Gradient Descent
Derivatives of Stochastic Gradient Descent
F. Iutzeler
Edouard Pauwels
Samuel Vaiter
42
1
0
24 May 2024
Kronecker-Factored Approximate Curvature for Physics-Informed Neural
  Networks
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks
Felix Dangel
Johannes Müller
Marius Zeinhofer
ODL
34
6
0
24 May 2024
Exact Gauss-Newton Optimization for Training Deep Neural Networks
Exact Gauss-Newton Optimization for Training Deep Neural Networks
Mikalai Korbit
Adeyemi Damilare Adeoye
Alberto Bemporad
Mario Zanon
ODL
33
0
0
23 May 2024
Thermodynamic Natural Gradient Descent
Thermodynamic Natural Gradient Descent
Kaelan Donatella
Samuel Duffield
Maxwell Aifer
Denis Melanson
Gavin Crooks
Patrick J. Coles
28
3
0
22 May 2024
Almost sure convergence rates of stochastic gradient methods under gradient domination
Almost sure convergence rates of stochastic gradient methods under gradient domination
Simon Weissmann
Sara Klein
Waïss Azizian
Leif Döring
39
3
0
22 May 2024
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov
  Optimization Approach
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach
Chung-Hsuan Hu
Zheng Chen
Erik G. Larsson
39
2
0
20 May 2024
Minimisation of Polyak-Łojasewicz Functions Using Random Zeroth-Order
  Oracles
Minimisation of Polyak-Łojasewicz Functions Using Random Zeroth-Order Oracles
Amir Ali Farzin
Iman Shames
28
1
0
15 May 2024
Robust Semi-supervised Learning by Wisely Leveraging Open-set Data
Robust Semi-supervised Learning by Wisely Leveraging Open-set Data
Yang Yang
Nan Jiang
Yi Tian Xu
De-Chuan Zhan
36
17
0
11 May 2024
Optimal Baseline Corrections for Off-Policy Contextual Bandits
Optimal Baseline Corrections for Off-Policy Contextual Bandits
Shashank Gupta
Olivier Jeunen
Harrie Oosterhuis
Maarten de Rijke
36
7
0
09 May 2024
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based
  Meta-solving
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving
S. Arisaka
Qianxiao Li
27
0
0
05 May 2024
A Full Adagrad algorithm with O(Nd) operations
A Full Adagrad algorithm with O(Nd) operations
Antoine Godichon-Baggioni
Wei Lu
Bruno Portier
ODL
54
0
0
03 May 2024
The Privacy Power of Correlated Noise in Decentralized Learning
The Privacy Power of Correlated Noise in Decentralized Learning
Youssef Allouah
Anastasia Koloskova
Aymane El Firdoussi
Martin Jaggi
R. Guerraoui
31
4
0
02 May 2024
On the Relevance of Byzantine Robust Optimization Against Data Poisoning
On the Relevance of Byzantine Robust Optimization Against Data Poisoning
Sadegh Farhadkhani
R. Guerraoui
Nirupam Gupta
Rafael Pinot
AAML
27
1
0
01 May 2024
IID Relaxation by Logical Expressivity: A Research Agenda for Fitting
  Logics to Neurosymbolic Requirements
IID Relaxation by Logical Expressivity: A Research Agenda for Fitting Logics to Neurosymbolic Requirements
M. Stol
Alessandra Mileo
34
1
0
30 Apr 2024
Advancing Supervised Learning with the Wave Loss Function: A Robust and
  Smooth Approach
Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach
M. Akhtar
Muhammad Tanveer
Mohd. Arshad
27
17
0
28 Apr 2024
Second-order Information Promotes Mini-Batch Robustness in
  Variance-Reduced Gradients
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients
Sachin Garg
A. Berahas
Michal Dereziñski
46
1
0
23 Apr 2024
Rate Analysis of Coupled Distributed Stochastic Approximation for
  Misspecified Optimization
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization
Yaqun Yang
Jinlong Lei
26
0
0
21 Apr 2024
FedMeS: Personalized Federated Continual Learning Leveraging Local
  Memory
FedMeS: Personalized Federated Continual Learning Leveraging Local Memory
Jingru Xie
Chenqi Zhu
Songze Li
FedML
CLL
32
0
0
19 Apr 2024
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