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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
Quantized Convolutional Neural Networks Through the Lens of Partial
  Differential Equations
Quantized Convolutional Neural Networks Through the Lens of Partial Differential EquationsResearch in the Mathematical Sciences (Res. Math. Sci.), 2021
Ido Ben-Yair
Gil Ben Shalom
Moshe Eliasof
Eran Treister
MQ
277
5
0
31 Aug 2021
Approximate Bayesian Optimisation for Neural Networks
Approximate Bayesian Optimisation for Neural Networks
N. Hassen
Irina Rish
128
1
0
27 Aug 2021
The Number of Steps Needed for Nonconvex Optimization of a Deep Learning
  Optimizer is a Rational Function of Batch Size
The Number of Steps Needed for Nonconvex Optimization of a Deep Learning Optimizer is a Rational Function of Batch Size
Hideaki Iiduka
222
2
0
26 Aug 2021
Adaptive shot allocation for fast convergence in variational quantum
  algorithms
Adaptive shot allocation for fast convergence in variational quantum algorithms
Andi Gu
Angus Lowe
Pavel A. Dub
Patrick J. Coles
A. Arrasmith
179
27
0
23 Aug 2021
Anarchic Federated Learning
Anarchic Federated LearningInternational Conference on Machine Learning (ICML), 2021
Haibo Yang
Xin Zhang
Prashant Khanduri
Jia Liu
FedML
213
62
0
23 Aug 2021
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless
  Networks
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks
Chenyuan Feng
Heng Yang
Deshun Hu
Zhiwei Zhao
Tony Q.S. Quek
Geyong Min
243
111
0
20 Aug 2021
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and
  Horizontal Data Partitioning
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning
Anirban Das
Timothy Castiglia
Maroun Touma
S. Patterson
FedML
330
25
0
19 Aug 2021
A proof of convergence for the gradient descent optimization method with
  random initializations in the training of neural networks with ReLU
  activation for piecewise linear target functions
A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functionsJournal of machine learning research (JMLR), 2021
Arnulf Jentzen
Adrian Riekert
227
19
0
10 Aug 2021
On the Hyperparameters in Stochastic Gradient Descent with Momentum
On the Hyperparameters in Stochastic Gradient Descent with MomentumJournal of machine learning research (JMLR), 2021
Bin Shi
248
20
0
09 Aug 2021
Uniform Sampling over Episode Difficulty
Uniform Sampling over Episode Difficulty
Sébastien M. R. Arnold
Guneet Singh Dhillon
Avinash Ravichandran
Stefano Soatto
194
14
0
03 Aug 2021
Numerical Solution of Stiff ODEs with Physics-Informed RPNNs
Numerical Solution of Stiff ODEs with Physics-Informed RPNNs
Evangelos Galaris
Gianluca Fabiani
Francesco Calabrò
D. Serafino
Constantinos Siettos
208
3
0
03 Aug 2021
Coordinate descent on the orthogonal group for recurrent neural network
  training
Coordinate descent on the orthogonal group for recurrent neural network trainingAAAI Conference on Artificial Intelligence (AAAI), 2021
E. Massart
V. Abrol
222
13
0
30 Jul 2021
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient
  Distributed Learning
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed LearningIEEE International Conference on Mobile Adhoc and Sensor Systems (MASS), 2021
Guangfeng Yan
Shao-Lun Huang
Tian-Shing Lan
Linqi Song
MQ
125
8
0
30 Jul 2021
Decentralized Federated Learning: Balancing Communication and Computing
  Costs
Decentralized Federated Learning: Balancing Communication and Computing CostsIEEE Transactions on Signal and Information Processing over Networks (TSIPN), 2021
Wei Liu
Li Chen
Wenyi Zhang
FedML
380
140
0
26 Jul 2021
A general sample complexity analysis of vanilla policy gradient
A general sample complexity analysis of vanilla policy gradientInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Rui Yuan
Robert Mansel Gower
A. Lazaric
460
82
0
23 Jul 2021
Improved Learning Rates for Stochastic Optimization: Two Theoretical
  Viewpoints
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints
Shaojie Li
Yong Liu
329
14
0
19 Jul 2021
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and
  Reliability
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability
Qiyiwen Zhang
Zhiqi Bu
Kan Chen
Qi Long
BDLUQCV
222
12
0
18 Jul 2021
Globally Convergent Multilevel Training of Deep Residual Networks
Globally Convergent Multilevel Training of Deep Residual Networks
Alena Kopanicáková
Rolf Krause
343
19
0
15 Jul 2021
Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines
Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines
Shigang Li
Torsten Hoefler
AI4CELRMGNN
498
165
0
14 Jul 2021
Nonlinear Least Squares for Large-Scale Machine Learning using
  Stochastic Jacobian Estimates
Nonlinear Least Squares for Large-Scale Machine Learning using Stochastic Jacobian Estimates
Johannes J Brust
199
2
0
12 Jul 2021
The Bayesian Learning Rule
The Bayesian Learning RuleJournal of machine learning research (JMLR), 2021
Mohammad Emtiyaz Khan
Håvard Rue
BDL
546
105
0
09 Jul 2021
Activated Gradients for Deep Neural Networks
Activated Gradients for Deep Neural NetworksIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
Mei Liu
Liangming Chen
Xiaohao Du
Long Jin
Mingsheng Shang
ODLAI4CE
165
198
0
09 Jul 2021
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity
A. Davtyan
Sepehr Sameni
L. Cerkezi
Givi Meishvili
Adam Bielski
Paolo Favaro
ODL
403
4
0
07 Jul 2021
KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural
  Networks
KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networks
J. G. Pauloski
Qi Huang
Lei Huang
Shivaram Venkataraman
Kyle Chard
Ian Foster
Zhao-jie Zhang
260
31
0
04 Jul 2021
A Comparison of the Delta Method and the Bootstrap in Deep Learning
  Classification
A Comparison of the Delta Method and the Bootstrap in Deep Learning Classification
G. K. Nilsen
A. Munthe-Kaas
H. Skaug
M. Brun
119
0
0
04 Jul 2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth
  Games: Convergence Analysis under Expected Co-coercivity
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivityNeural Information Processing Systems (NeurIPS), 2021
Nicolas Loizou
Hugo Berard
Gauthier Gidel
Alexia Jolicoeur-Martineau
Damien Scieur
382
60
0
30 Jun 2021
Never Go Full Batch (in Stochastic Convex Optimization)
Never Go Full Batch (in Stochastic Convex Optimization)Neural Information Processing Systems (NeurIPS), 2021
I Zaghloul Amir
Y. Carmon
Tomer Koren
Roi Livni
225
14
0
29 Jun 2021
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension
  Dependence
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence
Daogao Liu
Zhou Lu
LRM
73
2
0
28 Jun 2021
A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear
  Equality Constrained Optimization with Rank-Deficient Jacobians
A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear Equality Constrained Optimization with Rank-Deficient Jacobians
A. Berahas
Frank E. Curtis
Michael OÑeill
Daniel P. Robinson
200
44
0
24 Jun 2021
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman
  Operators
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators
Zaiwei Chen
S. T. Maguluri
Sanjay Shakkottai
Karthikeyan Shanmugam
OffRL
171
16
0
24 Jun 2021
Numerical influence of ReLU'(0) on backpropagation
Numerical influence of ReLU'(0) on backpropagation
David Bertoin
Jérôme Bolte
Sébastien Gerchinovitz
Edouard Pauwels
228
0
0
23 Jun 2021
Solving Stochastic Optimization with Expectation Constraints Efficiently
  by a Stochastic Augmented Lagrangian-Type Algorithm
Solving Stochastic Optimization with Expectation Constraints Efficiently by a Stochastic Augmented Lagrangian-Type AlgorithmINFORMS journal on computing (IJOC), 2021
Liwei Zhang
Yule Zhang
Jia Wu
X. Xiao
187
16
0
22 Jun 2021
Memory Augmented Optimizers for Deep Learning
Memory Augmented Optimizers for Deep LearningInternational Conference on Learning Representations (ICLR), 2021
Paul-Aymeric McRae
Prasanna Parthasarathi
Mahmoud Assran
Sarath Chandar
ODL
145
6
0
20 Jun 2021
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal
  Sample and Communication Complexities for Federated Learning
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated LearningNeural Information Processing Systems (NeurIPS), 2021
Prashant Khanduri
Pranay Sharma
Haibo Yang
Min-Fong Hong
Jia Liu
K. Rajawat
P. Varshney
FedML
117
72
0
19 Jun 2021
Interval and fuzzy physics-informed neural networks for uncertain fields
Interval and fuzzy physics-informed neural networks for uncertain fieldsProbabilistic Engineering Mechanics (PEM), 2021
J. Fuhg
Ioannis Kalogeris
A. Fau
N. Bouklas
AI4CE
224
21
0
18 Jun 2021
Algorithmic Bias and Data Bias: Understanding the Relation between
  Distributionally Robust Optimization and Data Curation
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation
Agnieszka Słowik
Léon Bottou
FaML
217
20
0
17 Jun 2021
Masked Training of Neural Networks with Partial Gradients
Masked Training of Neural Networks with Partial Gradients
Amirkeivan Mohtashami
Martin Jaggi
Sebastian U. Stich
373
28
0
16 Jun 2021
A Survey on Fault-tolerance in Distributed Optimization and Machine
  Learning
A Survey on Fault-tolerance in Distributed Optimization and Machine Learning
Shuo Liu
AI4CEOOD
233
14
0
16 Jun 2021
On the Sample Complexity and Metastability of Heavy-tailed Policy Search
  in Continuous Control
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
Amrit Singh Bedi
Anjaly Parayil
Junyu Zhang
Mengdi Wang
Alec Koppel
177
16
0
15 Jun 2021
RCURRENCY: Live Digital Asset Trading Using a Recurrent Neural
  Network-based Forecasting System
RCURRENCY: Live Digital Asset Trading Using a Recurrent Neural Network-based Forecasting System
Yapeng Hu
R. V. Gurp
Ashay Somai
H. Kooijman
Jan S. Rellermeyer
85
0
0
13 Jun 2021
Federated Learning with Buffered Asynchronous Aggregation
Federated Learning with Buffered Asynchronous AggregationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021
John Nguyen
Kshitiz Malik
Hongyuan Zhan
Ashkan Yousefpour
Michael G. Rabbat
Mani Malek
Dzmitry Huba
FedML
396
407
0
11 Jun 2021
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous
  Distributed Learning
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
Eugene Belilovsky
Louis Leconte
Lucas Caccia
Michael Eickenberg
Edouard Oyallon
169
9
0
11 Jun 2021
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm
  via Langevin Monte Carlo within Gibbs
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within GibbsInternational Conference on Machine Learning (ICML), 2021
Vincent Plassier
Maxime Vono
Alain Durmus
Eric Moulines
273
18
0
11 Jun 2021
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max
  Optimization Problems
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization ProblemsSignal Processing (Signal Process.), 2021
Babak Barazandeh
Tianjian Huang
George Michailidis
238
13
0
10 Jun 2021
A Continuized View on Nesterov Acceleration for Stochastic Gradient
  Descent and Randomized Gossip
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip
Mathieu Even
Raphael Berthier
Francis R. Bach
Nicolas Flammarion
Pierre Gaillard
Aymeric Dieuleveut
Laurent Massoulié
Adrien B. Taylor
303
24
0
10 Jun 2021
The dilemma of quantum neural networks
The dilemma of quantum neural networksIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021
Yan Qian
Xinbiao Wang
Yuxuan Du
Xingyao Wu
Dacheng Tao
165
40
0
09 Jun 2021
Asynchronous Distributed Optimization with Redundancy in Cost Functions
Asynchronous Distributed Optimization with Redundancy in Cost Functions
Shuo Liu
Nirupam Gupta
Nitin H. Vaidya
269
3
0
07 Jun 2021
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic ModelsNeural Information Processing Systems (NeurIPS), 2021
Courtney Paquette
Elliot Paquette
ODL
197
17
0
07 Jun 2021
Stein ICP for Uncertainty Estimation in Point Cloud Matching
Stein ICP for Uncertainty Estimation in Point Cloud MatchingIEEE Robotics and Automation Letters (RA-L), 2021
F. A. Maken
Fabio Ramos
Lionel Ott
3DV3DPC
221
34
0
07 Jun 2021
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural
  Networks
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
Chaoyang He
Emir Ceyani
Keshav Balasubramanian
M. Annavaram
Salman Avestimehr
FedML
196
62
0
04 Jun 2021
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