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Optimization Methods for Large-Scale Machine Learning
v1v2v3 (latest)

Optimization Methods for Large-Scale Machine Learning

15 June 2016
Léon Bottou
Frank E. Curtis
J. Nocedal
ArXiv (abs)PDFHTML

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

50 / 867 papers shown
Title
First-Order Preconditioning via Hypergradient Descent
First-Order Preconditioning via Hypergradient Descent
Theodore H. Moskovitz
Rui Wang
Janice Lan
Sanyam Kapoor
Thomas Miconi
J. Yosinski
Aditya Rawal
AI4CE
79
8
0
18 Oct 2019
Improving the convergence of SGD through adaptive batch sizes
Improving the convergence of SGD through adaptive batch sizes
Scott Sievert
Zachary B. Charles
ODL
74
8
0
18 Oct 2019
Error Lower Bounds of Constant Step-size Stochastic Gradient Descent
Error Lower Bounds of Constant Step-size Stochastic Gradient Descent
Zhiyan Ding
Yiding Chen
Qin Li
Xiaojin Zhu
54
4
0
18 Oct 2019
Adaptive Step Sizes in Variance Reduction via Regularization
Adaptive Step Sizes in Variance Reduction via Regularization
Bingcong Li
G. Giannakis
50
5
0
15 Oct 2019
Predicting dynamical system evolution with residual neural networks
Predicting dynamical system evolution with residual neural networks
Artem Chashchin
M. Botchev
Ivan Oseledets
G. Ovchinnikov
AI4TSAI4CE
45
3
0
11 Oct 2019
Straggler-Agnostic and Communication-Efficient Distributed Primal-Dual
  Algorithm for High-Dimensional Data Mining
Straggler-Agnostic and Communication-Efficient Distributed Primal-Dual Algorithm for High-Dimensional Data Mining
Zhouyuan Huo
Heng-Chiao Huang
FedML
47
5
0
09 Oct 2019
The Complexity of Finding Stationary Points with Stochastic Gradient
  Descent
The Complexity of Finding Stationary Points with Stochastic Gradient Descent
Yoel Drori
Shigehito Shimizu
94
64
0
04 Oct 2019
Partial differential equation regularization for supervised machine
  learning
Partial differential equation regularization for supervised machine learning
Jillian R. Fisher
56
2
0
03 Oct 2019
SlowMo: Improving Communication-Efficient Distributed SGD with Slow
  Momentum
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
Jianyu Wang
Vinayak Tantia
Nicolas Ballas
Michael G. Rabbat
99
201
0
01 Oct 2019
Conservative set valued fields, automatic differentiation, stochastic
  gradient method and deep learning
Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning
Jérôme Bolte
Edouard Pauwels
104
129
0
23 Sep 2019
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs
Xuhui Meng
Zhen Li
Dongkun Zhang
George Karniadakis
PINNAI4CE
90
457
0
23 Sep 2019
Human Position Detection & Tracking with On-robot Time-of-Flight Laser
  Ranging Sensors
Human Position Detection & Tracking with On-robot Time-of-Flight Laser Ranging Sensors
Sarthak Arora
Shitij Kumar
F. Sahin
14
2
0
21 Sep 2019
Empirical study towards understanding line search approximations for
  training neural networks
Empirical study towards understanding line search approximations for training neural networks
Younghwan Chae
D. Wilke
110
11
0
15 Sep 2019
Ouroboros: On Accelerating Training of Transformer-Based Language Models
Ouroboros: On Accelerating Training of Transformer-Based Language Models
Qian Yang
Zhouyuan Huo
Wenlin Wang
Heng-Chiao Huang
Lawrence Carin
57
9
0
14 Sep 2019
Shapley Interpretation and Activation in Neural Networks
Shapley Interpretation and Activation in Neural Networks
Yadong Li
Xin Cui
TDIFAttLLMSV
50
3
0
13 Sep 2019
The Error-Feedback Framework: Better Rates for SGD with Delayed
  Gradients and Compressed Communication
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication
Sebastian U. Stich
Sai Praneeth Karimireddy
FedML
80
20
0
11 Sep 2019
Efficient Continual Learning in Neural Networks with Embedding
  Regularization
Efficient Continual Learning in Neural Networks with Embedding Regularization
Jary Pomponi
Simone Scardapane
Vincenzo Lomonaco
A. Uncini
CLL
87
42
0
09 Sep 2019
Distributed Deep Learning with Event-Triggered Communication
Distributed Deep Learning with Event-Triggered Communication
Jemin George
Prudhvi K. Gurram
56
16
0
08 Sep 2019
Distributed Training of Embeddings using Graph Analytics
Distributed Training of Embeddings using Graph Analytics
G. Gill
Roshan Dathathri
Saeed Maleki
Madan Musuvathi
Todd Mytkowicz
Olli Saarikivi The University of Texas at Austin
GNN
26
1
0
08 Sep 2019
Stochastic quasi-Newton with line-search regularization
Stochastic quasi-Newton with line-search regularization
A. Wills
Thomas B. Schon
ODL
68
22
0
03 Sep 2019
Linear Convergence of Adaptive Stochastic Gradient Descent
Linear Convergence of Adaptive Stochastic Gradient Descent
Yuege Xie
Xiaoxia Wu
Rachel A. Ward
74
45
0
28 Aug 2019
Tackling Algorithmic Bias in Neural-Network Classifiers using
  Wasserstein-2 Regularization
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization
Laurent Risser
Alberto González Sanz
Quentin Vincenot
Jean-Michel Loubes
97
21
0
15 Aug 2019
Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data
  Classification
Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification
Chen Wang
Qin Yu
Kai Zhou
D. Hui
Xiaofeng Gong
Ruisen Luo
141
22
0
09 Aug 2019
Bias of Homotopic Gradient Descent for the Hinge Loss
Bias of Homotopic Gradient Descent for the Hinge Loss
Denali Molitor
Deanna Needell
Rachel A. Ward
39
6
0
26 Jul 2019
Learning the Tangent Space of Dynamical Instabilities from Data
Learning the Tangent Space of Dynamical Instabilities from Data
Antoine Blanchard
T. Sapsis
134
8
0
24 Jul 2019
Mix and Match: An Optimistic Tree-Search Approach for Learning Models
  from Mixture Distributions
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
Matthew Faw
Rajat Sen
Karthikeyan Shanmugam
Constantine Caramanis
Sanjay Shakkottai
80
3
0
23 Jul 2019
Bilevel Optimization, Deep Learning and Fractional Laplacian
  Regularization with Applications in Tomography
Bilevel Optimization, Deep Learning and Fractional Laplacian Regularization with Applications in Tomography
Harbir Antil
Z. Di
R. Khatri
58
51
0
22 Jul 2019
Speeding Up Iterative Closest Point Using Stochastic Gradient Descent
Speeding Up Iterative Closest Point Using Stochastic Gradient Descent
F. A. Maken
F. Ramos
Lionel Ott
3DPC
37
13
0
22 Jul 2019
Adaptive Weight Decay for Deep Neural Networks
Adaptive Weight Decay for Deep Neural Networks
Kensuke Nakamura
Byung-Woo Hong
63
43
0
21 Jul 2019
Techniques for Automated Machine Learning
Techniques for Automated Machine Learning
Yi-Wei Chen
Qingquan Song
Helen Zhou
65
50
0
21 Jul 2019
An Evolutionary Algorithm of Linear complexity: Application to Training
  of Deep Neural Networks
An Evolutionary Algorithm of Linear complexity: Application to Training of Deep Neural Networks
S. I. Valdez
A. R. Domínguez
ODL
29
1
0
12 Jul 2019
Adaptive Deep Learning for High-Dimensional Hamilton-Jacobi-Bellman
  Equations
Adaptive Deep Learning for High-Dimensional Hamilton-Jacobi-Bellman Equations
Tenavi Nakamura-Zimmerer
Q. Gong
W. Kang
134
134
0
11 Jul 2019
The stochastic multi-gradient algorithm for multi-objective optimization
  and its application to supervised machine learning
The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning
Suyun Liu
Luis Nunes Vicente
167
75
0
10 Jul 2019
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk
  Minimization
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization
Kenji Kawaguchi
Haihao Lu
ODL
100
64
0
09 Jul 2019
Unified Optimal Analysis of the (Stochastic) Gradient Method
Unified Optimal Analysis of the (Stochastic) Gradient Method
Sebastian U. Stich
91
113
0
09 Jul 2019
Learning joint lesion and tissue segmentation from task-specific
  hetero-modal datasets
Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets
Reuben Dorent
Wenqi Li
J. Ekanayake
Sebastien Ourselin
Tom Vercauteren
55
4
0
07 Jul 2019
ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs
ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs
B. Grimstad
H. Andersson
64
144
0
06 Jul 2019
Precision annealing Monte Carlo methods for statistical data
  assimilation and machine learning
Precision annealing Monte Carlo methods for statistical data assimilation and machine learning
Zheng Fang
Adrian S. Wong
Kangbo Hao
Alexander J. A. Ty
H. Abarbanel
26
1
0
06 Jul 2019
Variance Reduction for Matrix Games
Variance Reduction for Matrix Games
Y. Carmon
Yujia Jin
Aaron Sidford
Kevin Tian
94
67
0
03 Jul 2019
Globally Convergent Newton Methods for Ill-conditioned Generalized
  Self-concordant Losses
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
Ulysse Marteau-Ferey
Francis R. Bach
Alessandro Rudi
56
36
0
03 Jul 2019
The Role of Memory in Stochastic Optimization
The Role of Memory in Stochastic Optimization
Antonio Orvieto
Jonas Köhler
Aurelien Lucchi
94
31
0
02 Jul 2019
Network-accelerated Distributed Machine Learning Using MLFabric
Network-accelerated Distributed Machine Learning Using MLFabric
Raajay Viswanathan
Aditya Akella
AI4CE
42
4
0
30 Jun 2019
Combining Stochastic Adaptive Cubic Regularization with Negative
  Curvature for Nonconvex Optimization
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization
Seonho Park
Seung Hyun Jung
P. Pardalos
ODL
72
15
0
27 Jun 2019
A Review on Deep Learning in Medical Image Reconstruction
A Review on Deep Learning in Medical Image Reconstruction
Hai-Miao Zhang
Bin Dong
MedIm
128
128
0
23 Jun 2019
A Unifying Framework for Variance Reduction Algorithms for Finding
  Zeroes of Monotone Operators
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators
Xun Zhang
W. Haskell
Z. Ye
51
3
0
22 Jun 2019
Fully Decoupled Neural Network Learning Using Delayed Gradients
Fully Decoupled Neural Network Learning Using Delayed Gradients
Huiping Zhuang
Yi Wang
Qinglai Liu
Shuai Zhang
Zhiping Lin
FedML
83
31
0
21 Jun 2019
A Survey of Optimization Methods from a Machine Learning Perspective
A Survey of Optimization Methods from a Machine Learning Perspective
Shiliang Sun
Zehui Cao
Han Zhu
Jing Zhao
88
566
0
17 Jun 2019
Optimizing Pipelined Computation and Communication for
  Latency-Constrained Edge Learning
Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning
N. Skatchkovsky
Osvaldo Simeone
52
17
0
11 Jun 2019
Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and
  Sparse Neural Network Training
Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training
Paul Grigas
Alfonso Lobos
Nathan Vermeersch
86
5
0
09 Jun 2019
Practical Deep Learning with Bayesian Principles
Practical Deep Learning with Bayesian Principles
Kazuki Osawa
S. Swaroop
Anirudh Jain
Runa Eschenhagen
Richard Turner
Rio Yokota
Mohammad Emtiyaz Khan
BDLUQCV
167
247
0
06 Jun 2019
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