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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 / 866 papers shown
Title
Doing More with Less: Overcoming Data Scarcity for POI Recommendation
  via Cross-Region Transfer
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer
Vinayak Gupta
Srikanta J. Bedathur
92
19
0
16 Jan 2022
Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired
  Simulation Approach
Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach
T. Wan
L. Hong
26
11
0
15 Jan 2022
Federated Optimization of Smooth Loss Functions
Federated Optimization of Smooth Loss Functions
Ali Jadbabaie
A. Makur
Devavrat Shah
FedML
438
7
0
06 Jan 2022
Asymptotics of $\ell_2$ Regularized Network Embeddings
Asymptotics of ℓ2\ell_2ℓ2​ Regularized Network Embeddings
A. Davison
96
0
0
05 Jan 2022
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Boxin Zhao
Lingxiao Wang
Mladen Kolar
Ziqi Liu
Qing Cui
Jun Zhou
Chaochao Chen
FedML
162
11
0
28 Dec 2021
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional
  partial differential equations
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations
Keju Tang
Xiaoliang Wan
Chao Yang
77
116
0
28 Dec 2021
Wireless-Enabled Asynchronous Federated Fourier Neural Network for
  Turbulence Prediction in Urban Air Mobility (UAM)
Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)
Tengchan Zeng
Omid Semiari
Walid Saad
M. Bennis
65
3
0
26 Dec 2021
Improving Robustness with Image Filtering
Improving Robustness with Image Filtering
M. Terzi
Mattia Carletti
Gian Antonio Susto
AAML
60
0
0
21 Dec 2021
Accurate Neural Training with 4-bit Matrix Multiplications at Standard
  Formats
Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats
Brian Chmiel
Ron Banner
Elad Hoffer
Hilla Ben Yaacov
Daniel Soudry
MQ
74
24
0
19 Dec 2021
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive
  Survey
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey
Jiaoyan Chen
Yuxia Geng
Zhuo Chen
Jeff Z. Pan
Yuan He
Wen Zhang
Ian Horrocks
Hua-zeng Chen
134
49
0
18 Dec 2021
Minimization of Stochastic First-order Oracle Complexity of Adaptive
  Methods for Nonconvex Optimization
Minimization of Stochastic First-order Oracle Complexity of Adaptive Methods for Nonconvex Optimization
Hideaki Iiduka
43
0
0
14 Dec 2021
Convergence proof for stochastic gradient descent in the training of
  deep neural networks with ReLU activation for constant target functions
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions
Martin Hutzenthaler
Arnulf Jentzen
Katharina Pohl
Adrian Riekert
Luca Scarpa
MLT
114
7
0
13 Dec 2021
A Novel Sequential Coreset Method for Gradient Descent Algorithms
A Novel Sequential Coreset Method for Gradient Descent Algorithms
Jiawei Huang
Ru Huang
Wenjie Liu
N. Freris
Huihua Ding
95
16
0
05 Dec 2021
Regularized Newton Method with Global $O(1/k^2)$ Convergence
Regularized Newton Method with Global O(1/k2)O(1/k^2)O(1/k2) Convergence
Konstantin Mishchenko
87
41
0
03 Dec 2021
On Large Batch Training and Sharp Minima: A Fokker-Planck Perspective
On Large Batch Training and Sharp Minima: A Fokker-Planck Perspective
Xiaowu Dai
Yuhua Zhu
49
4
0
02 Dec 2021
Improving Differentially Private SGD via Randomly Sparsified Gradients
Improving Differentially Private SGD via Randomly Sparsified Gradients
Junyi Zhu
Matthew B. Blaschko
78
5
0
01 Dec 2021
An Optimization Framework for Federated Edge Learning
An Optimization Framework for Federated Edge Learning
Yangchen Li
Ying Cui
Vincent K. N. Lau
FedML
56
7
0
26 Nov 2021
Random-reshuffled SARAH does not need a full gradient computations
Random-reshuffled SARAH does not need a full gradient computations
Aleksandr Beznosikov
Martin Takáč
74
8
0
26 Nov 2021
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning
  Rule
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule
Miao Zhang
Jilin Hu
Steven W. Su
Shirui Pan
Xiaojun Chang
B. Yang
Gholamreza Haffari
OOD
115
15
0
25 Nov 2021
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning
Siladittya Manna
Umapada Pal
Saumik Bhattacharya
SSL
123
1
0
24 Nov 2021
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise
  Learning
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning
Zhenhuan Yang
Yunwen Lei
Puyu Wang
Tianbao Yang
Yiming Ying
80
26
0
23 Nov 2021
Variance Reduction in Deep Learning: More Momentum is All You Need
Variance Reduction in Deep Learning: More Momentum is All You Need
Lionel Tondji
S. Kashubin
Moustapha Cissé
ODL
42
1
0
23 Nov 2021
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent:
  Convergence Guarantees and Empirical Benefits
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Hao Chen
Lili Zheng
Raed Al Kontar
Garvesh Raskutti
86
3
0
19 Nov 2021
Stationary Behavior of Constant Stepsize SGD Type Algorithms: An
  Asymptotic Characterization
Stationary Behavior of Constant Stepsize SGD Type Algorithms: An Asymptotic Characterization
Zaiwei Chen
Shancong Mou
S. T. Maguluri
55
13
0
11 Nov 2021
AGGLIO: Global Optimization for Locally Convex Functions
AGGLIO: Global Optimization for Locally Convex Functions
Debojyoti Dey
B. Mukhoty
Purushottam Kar
62
2
0
06 Nov 2021
Large-Scale Deep Learning Optimizations: A Comprehensive Survey
Large-Scale Deep Learning Optimizations: A Comprehensive Survey
Xiaoxin He
Fuzhao Xue
Xiaozhe Ren
Yang You
90
15
0
01 Nov 2021
Multi-Task Learning based Convolutional Models with Curriculum Learning
  for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow
Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow
Haitz Sáez de Ocáriz Borde
David Sondak
P. Protopapas
AI4CE
47
3
0
30 Oct 2021
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by
  Finding Flat Minima
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima
Guangyuan Shi
Jiaxin Chen
Wenlong Zhang
Li-Ming Zhan
Xiao-Ming Wu
CLL
181
160
0
30 Oct 2021
Dynamic Differential-Privacy Preserving SGD
Dynamic Differential-Privacy Preserving SGD
Jian Du
Song Li
Xiangyi Chen
Siheng Chen
Mingyi Hong
92
33
0
30 Oct 2021
Efficient Meta Subspace Optimization
Efficient Meta Subspace Optimization
Yoni Choukroun
Michael Katz
85
1
0
28 Oct 2021
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic
  Gradient Descent
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
Sharan Vaswani
Benjamin Dubois-Taine
Reza Babanezhad
98
13
0
21 Oct 2021
Boosting Resource-Constrained Federated Learning Systems with Guessed Updates
Boosting Resource-Constrained Federated Learning Systems with Guessed Updates
Mohamed Yassine Boukhari
Akash Dhasade
Anne-Marie Kermarrec
Rafael Pires
Othmane Safsafi
Rishi Sharma
FedML
91
0
0
21 Oct 2021
A Data-Centric Optimization Framework for Machine Learning
A Data-Centric Optimization Framework for Machine Learning
Oliver Rausch
Tal Ben-Nun
Nikoli Dryden
Andrei Ivanov
Shigang Li
Torsten Hoefler
AI4CE
57
16
0
20 Oct 2021
Optimal randomized classification trees
Optimal randomized classification trees
R. Blanquero
E. Carrizosa
Antonios Tsourdos
Dolores Romero Morales
228
47
0
19 Oct 2021
Accelerating Training and Inference of Graph Neural Networks with Fast
  Sampling and Pipelining
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
Tim Kaler
Nickolas Stathas
Anne Ouyang
A. Iliopoulos
Tao B. Schardl
C. E. Leiserson
Jie Chen
GNN
142
55
0
16 Oct 2021
Resource-constrained Federated Edge Learning with Heterogeneous Data:
  Formulation and Analysis
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis
Yi Liu
Yuanshao Zhu
James Jianqiao Yu
FedML
74
28
0
14 Oct 2021
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous
  Multi-GPU Servers
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers
Yujing Ma
Florin Rusu
Kesheng Wu
A. Sim
102
3
0
13 Oct 2021
Convergence of Random Reshuffling Under The Kurdyka-Łojasiewicz
  Inequality
Convergence of Random Reshuffling Under The Kurdyka-Łojasiewicz Inequality
Xiao Li
Andre Milzarek
Junwen Qiu
95
20
0
10 Oct 2021
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect
Yuqing Wang
Minshuo Chen
T. Zhao
Molei Tao
AI4CE
142
42
0
07 Oct 2021
On the Generalization of Models Trained with SGD: Information-Theoretic
  Bounds and Implications
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications
Ziqiao Wang
Yongyi Mao
FedMLMLT
124
26
0
07 Oct 2021
Inexact bilevel stochastic gradient methods for constrained and
  unconstrained lower-level problems
Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problems
Tommaso Giovannelli
G. Kent
Luis Nunes Vicente
125
12
0
01 Oct 2021
An Accelerated Stochastic Gradient for Canonical Polyadic Decomposition
An Accelerated Stochastic Gradient for Canonical Polyadic Decomposition
Ioanna Siaminou
A. Liavas
63
4
0
28 Sep 2021
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic
  Optimization
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization
Raghu Bollapragada
Stefan M. Wild
76
12
0
24 Sep 2021
Inequality Constrained Stochastic Nonlinear Optimization via Active-Set
  Sequential Quadratic Programming
Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming
Sen Na
M. Anitescu
Mladen Kolar
77
35
0
23 Sep 2021
AdaLoss: A computationally-efficient and provably convergent adaptive
  gradient method
AdaLoss: A computationally-efficient and provably convergent adaptive gradient method
Xiaoxia Wu
Yuege Xie
S. Du
Rachel A. Ward
ODL
49
7
0
17 Sep 2021
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic
  Reinforcement Learning and Global Convergence of Policy Gradient Methods
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods
Xin Guo
Anran Hu
Junzi Zhang
OffRL
86
6
0
13 Sep 2021
Byzantine-robust Federated Learning through Collaborative Malicious
  Gradient Filtering
Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering
Jian Xu
Shao-Lun Huang
Linqi Song
Tian-Shing Lan
FedMLAAML
85
48
0
13 Sep 2021
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order
  Information
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information
Majid Jahani
S. Rusakov
Zheng Shi
Peter Richtárik
Michael W. Mahoney
Martin Takávc
ODL
51
27
0
11 Sep 2021
Self-adaptive deep neural network: Numerical approximation to functions
  and PDEs
Self-adaptive deep neural network: Numerical approximation to functions and PDEs
Zhiqiang Cai
Jingshuang Chen
Min Liu
ODL
49
14
0
07 Sep 2021
Analytic natural gradient updates for Cholesky factor in Gaussian
  variational approximation
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation
Linda S. L. Tan
108
13
0
01 Sep 2021
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