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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 / 1,491 papers shown
Unified Optimal Analysis of the (Stochastic) Gradient Method
Unified Optimal Analysis of the (Stochastic) Gradient Method
Sebastian U. Stich
246
127
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 datasetsInternational Conference on Medical Imaging with Deep Learning (MIDL), 2019
Reuben Dorent
Wenqi Li
J. Ekanayake
Sebastien Ourselin
Tom Vercauteren
142
4
0
07 Jul 2019
ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs
ReLU Networks as Surrogate Models in Mixed-Integer Linear ProgramsComputers and Chemical Engineering (Comput. Chem. Eng.), 2019
B. Grimstad
H. Andersson
221
152
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 learningPhysical Review Research (PRR), 2019
Zheng Fang
Adrian S. Wong
Kangbo Hao
Alexander J. A. Ty
H. Abarbanel
122
1
0
06 Jul 2019
Variance Reduction for Matrix Games
Variance Reduction for Matrix Games
Y. Carmon
Yujia Jin
Aaron Sidford
Kevin Tian
284
74
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
206
39
0
03 Jul 2019
The Role of Memory in Stochastic Optimization
The Role of Memory in Stochastic OptimizationConference on Uncertainty in Artificial Intelligence (UAI), 2019
Antonio Orvieto
Jonas Köhler
Aurelien Lucchi
202
32
0
02 Jul 2019
Network-accelerated Distributed Machine Learning Using MLFabric
Network-accelerated Distributed Machine Learning Using MLFabric
Raajay Viswanathan
Aditya Akella
AI4CE
114
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 OptimizationJournal of Optimization Theory and Applications (JOTA), 2019
Seonho Park
Seung Hyun Jung
P. Pardalos
ODL
145
17
0
27 Jun 2019
A Review on Deep Learning in Medical Image Reconstruction
A Review on Deep Learning in Medical Image ReconstructionJournal of the Operations Research Society of China (JORSC), 2019
Hai-Miao Zhang
Bin Dong
MedIm
386
150
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
184
3
0
22 Jun 2019
Fully Decoupled Neural Network Learning Using Delayed Gradients
Fully Decoupled Neural Network Learning Using Delayed GradientsIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019
Huiping Zhuang
Yi Wang
Qinglai Liu
Shuai Zhang
Zhiping Lin
FedML
173
33
0
21 Jun 2019
Accelerating Mini-batch SARAH by Step Size Rules
Accelerating Mini-batch SARAH by Step Size RulesInformation Sciences (Inf. Sci.), 2019
Zhuang Yang
Zengping Chen
Cheng-Yu Wang
243
15
0
20 Jun 2019
A Survey of Optimization Methods from a Machine Learning Perspective
A Survey of Optimization Methods from a Machine Learning PerspectiveIEEE Transactions on Cybernetics (IEEE Trans. Cybern.), 2019
Shiliang Sun
Zehui Cao
Han Zhu
Jing Zhao
226
631
0
17 Jun 2019
Optimizing Pipelined Computation and Communication for
  Latency-Constrained Edge Learning
Optimizing Pipelined Computation and Communication for Latency-Constrained Edge LearningIEEE Communications Letters (IEEE Commun. Lett.), 2019
N. Skatchkovsky
Osvaldo Simeone
132
18
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
187
5
0
09 Jun 2019
Practical Deep Learning with Bayesian Principles
Practical Deep Learning with Bayesian PrinciplesNeural Information Processing Systems (NeurIPS), 2019
Kazuki Osawa
S. Swaroop
Anirudh Jain
Runa Eschenhagen
Richard Turner
Rio Yokota
Mohammad Emtiyaz Khan
BDLUQCV
447
267
0
06 Jun 2019
Efficient Subsampled Gauss-Newton and Natural Gradient Methods for
  Training Neural Networks
Efficient Subsampled Gauss-Newton and Natural Gradient Methods for Training Neural Networks
Yi Ren
Shiqian Ma
146
40
0
05 Jun 2019
On the Convergence of SARAH and Beyond
On the Convergence of SARAH and BeyondInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Bingcong Li
Meng Ma
G. Giannakis
220
31
0
05 Jun 2019
Approximate Inference Turns Deep Networks into Gaussian Processes
Approximate Inference Turns Deep Networks into Gaussian ProcessesNeural Information Processing Systems (NeurIPS), 2019
Mohammad Emtiyaz Khan
Alexander Immer
Ehsan Abedi
M. Korzepa
UQCVBDL
393
130
0
05 Jun 2019
The Secrets of Machine Learning: Ten Things You Wish You Had Known
  Earlier to be More Effective at Data Analysis
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis
Cynthia Rudin
David Carlson
HAI
178
39
0
04 Jun 2019
A Generic Acceleration Framework for Stochastic Composite Optimization
A Generic Acceleration Framework for Stochastic Composite OptimizationNeural Information Processing Systems (NeurIPS), 2019
A. Kulunchakov
Julien Mairal
375
46
0
03 Jun 2019
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed
  SR1
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1International Conference on Machine Learning, Optimization, and Data Science (MOD), 2019
Majid Jahani
M. Nazari
S. Rusakov
A. Berahas
Martin Takávc
275
16
0
30 May 2019
Limitations of the Empirical Fisher Approximation for Natural Gradient
  Descent
Limitations of the Empirical Fisher Approximation for Natural Gradient DescentNeural Information Processing Systems (NeurIPS), 2019
Frederik Kunstner
Lukas Balles
Philipp Hennig
493
249
0
29 May 2019
An Inertial Newton Algorithm for Deep Learning
An Inertial Newton Algorithm for Deep LearningJournal of machine learning research (JMLR), 2019
Camille Castera
Jérôme Bolte
Cédric Févotte
Edouard Pauwels
PINNODL
281
70
0
29 May 2019
Where is the Information in a Deep Neural Network?
Where is the Information in a Deep Neural Network?
Alessandro Achille
Giovanni Paolini
Stefano Soatto
396
91
0
29 May 2019
Sample Complexity of Sample Average Approximation for Conditional
  Stochastic Optimization
Sample Complexity of Sample Average Approximation for Conditional Stochastic OptimizationSIAM Journal on Optimization (SIOPT), 2019
Yifan Hu
Xin Chen
Niao He
261
39
0
28 May 2019
Recursive Estimation for Sparse Gaussian Process Regression
Recursive Estimation for Sparse Gaussian Process Regression
Manuel Schürch
Dario Azzimonti
A. Benavoli
Marco Zaffalon
175
39
0
28 May 2019
Finite-Sample Analysis of Nonlinear Stochastic Approximation with
  Applications in Reinforcement Learning
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning
Zaiwei Chen
Sheng Zhang
Thinh T. Doan
John-Paul Clarke
S. T. Maguluri
361
67
0
27 May 2019
Robustness of accelerated first-order algorithms for strongly convex
  optimization problems
Robustness of accelerated first-order algorithms for strongly convex optimization problemsIEEE Transactions on Automatic Control (IEEE TAC), 2019
Hesameddin Mohammadi
Meisam Razaviyayn
M. Jovanović
223
47
0
27 May 2019
Decentralized Bayesian Learning over Graphs
Decentralized Bayesian Learning over Graphs
Anusha Lalitha
Xinghan Wang
O. Kilinc
Y. Lu
T. Javidi
F. Koushanfar
FedML
203
27
0
24 May 2019
Leader Stochastic Gradient Descent for Distributed Training of Deep
  Learning Models: Extension
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: ExtensionNeural Information Processing Systems (NeurIPS), 2019
Yunfei Teng
Wenbo Gao
F. Chalus
A. Choromańska
Shiqian Ma
Adrian Weller
499
14
0
24 May 2019
Blockwise Adaptivity: Faster Training and Better Generalization in Deep
  Learning
Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning
Shuai Zheng
James T. Kwok
ODL
167
5
0
23 May 2019
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition
  Sampling
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition SamplingInternational Conference on Intelligent Cloud Computing (ICICC), 2019
Jianyu Wang
Anit Kumar Sahu
Zhouyi Yang
Gauri Joshi
S. Kar
358
176
0
23 May 2019
Adaptive norms for deep learning with regularized Newton methods
Adaptive norms for deep learning with regularized Newton methods
Jonas Köhler
Leonard Adolphs
Aurelien Lucchi
ODL
169
12
0
22 May 2019
LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and
  Communication-Efficient Distributed Learning
LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed LearningIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019
Jingjing Zhang
Osvaldo Simeone
216
36
0
22 May 2019
Sequential training algorithm for neural networks
Sequential training algorithm for neural networks
Jongrae Kim
37
1
0
17 May 2019
Efficient Optimization of Loops and Limits with Randomized Telescoping
  Sums
Efficient Optimization of Loops and Limits with Randomized Telescoping SumsInternational Conference on Machine Learning (ICML), 2019
Alex Beatson
Ryan P. Adams
157
21
0
16 May 2019
Client-Edge-Cloud Hierarchical Federated Learning
Client-Edge-Cloud Hierarchical Federated Learning
Lumin Liu
Jun Zhang
S. H. Song
Khaled B. Letaief
FedML
349
918
0
16 May 2019
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor
  Decomposition
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor DecompositionKnowledge Discovery and Data Mining (KDD), 2019
Anil R. Yelundur
Vineet Chaoji
Bamdev Mishra
189
7
0
15 May 2019
A Stochastic Gradient Method with Biased Estimation for Faster Nonconvex
  Optimization
A Stochastic Gradient Method with Biased Estimation for Faster Nonconvex OptimizationPacific Rim International Conference on Artificial Intelligence (PRICAI), 2019
Jia Bi
S. Gunn
177
4
0
13 May 2019
Budgeted Training: Rethinking Deep Neural Network Training Under
  Resource Constraints
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsInternational Conference on Learning Representations (ICLR), 2019
Mengtian Li
Ersin Yumer
Deva Ramanan
258
54
0
12 May 2019
On the Computation and Communication Complexity of Parallel SGD with
  Dynamic Batch Sizes for Stochastic Non-Convex Optimization
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex OptimizationInternational Conference on Machine Learning (ICML), 2019
Hao Yu
Rong Jin
212
52
0
10 May 2019
The sharp, the flat and the shallow: Can weakly interacting agents learn
  to escape bad minima?
The sharp, the flat and the shallow: Can weakly interacting agents learn to escape bad minima?
N. Kantas
P. Parpas
G. Pavliotis
ODL
116
8
0
10 May 2019
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
AutoAssist: A Framework to Accelerate Training of Deep Neural NetworksNeural Information Processing Systems (NeurIPS), 2019
Jiong Zhang
Hsiang-Fu Yu
Inderjit S. Dhillon
189
29
0
08 May 2019
Sparse multiresolution representations with adaptive kernels
Sparse multiresolution representations with adaptive kernelsIEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2019
Maria Peifer
Luiz F. O. Chamon
Santiago Paternain
Alejandro Ribeiro
152
4
0
07 May 2019
Estimate Sequences for Variance-Reduced Stochastic Composite
  Optimization
Estimate Sequences for Variance-Reduced Stochastic Composite OptimizationInternational Conference on Machine Learning (ICML), 2019
A. Kulunchakov
Julien Mairal
192
27
0
07 May 2019
An Adaptive Remote Stochastic Gradient Method for Training Neural
  Networks
An Adaptive Remote Stochastic Gradient Method for Training Neural Networks
Yushu Chen
Hao Jing
Wenlai Zhao
Zhiqiang Liu
Haohuan Fu
Lián Qiao
Wei Xue
Guangwen Yang
ODL
524
2
0
04 May 2019
New optimization algorithms for neural network training using operator
  splitting techniques
New optimization algorithms for neural network training using operator splitting techniquesNeural Networks (NN), 2019
C. Alecsa
Titus Pinta
Imre Boros
173
9
0
29 Apr 2019
Target-Based Temporal Difference Learning
Target-Based Temporal Difference Learning
Donghwan Lee
Niao He
OOD
163
33
0
24 Apr 2019
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