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Sparse Inverse Covariance Matrix Estimation Using Quadratic
  Approximation

Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation

13 June 2013
Cho-Jui Hsieh
Mátyás A. Sustik
Inderjit S. Dhillon
Pradeep Ravikumar
ArXivPDFHTML

Papers citing "Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation"

30 / 30 papers shown
Title
Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm
Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm
Chengjing Wang
Peipei Tang
Wen-Bin He
Meixia Lin
38
0
0
17 Aug 2023
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph
  Neural Networks
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Chenhui Deng
Xiuyu Li
Zhuobo Feng
Zhiru Zhang
AAML
58
22
0
30 Jan 2022
Distributed Estimation of Sparse Inverse Covariances
Distributed Estimation of Sparse Inverse Covariances
Tong Yao
S. Sundaram
23
1
0
24 Sep 2021
Certifiably Optimal Sparse Inverse Covariance Estimation
Certifiably Optimal Sparse Inverse Covariance Estimation
Dimitris Bertsimas
Jourdain Lamperski
J. Pauphilet
22
13
0
25 Jun 2019
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised
  Classification
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification
Xiang Gao
Wei Hu
Zongming Guo
GNN
20
1
0
23 Apr 2019
Capturing Between-Tasks Covariance and Similarities Using Multivariate
  Linear Mixed Models
Capturing Between-Tasks Covariance and Similarities Using Multivariate Linear Mixed Models
Aviv Navon
Saharon Rosset
13
0
0
10 Dec 2018
Learning graphs from data: A signal representation perspective
Learning graphs from data: A signal representation perspective
Xiaowen Dong
D. Thanou
Michael G. Rabbat
P. Frossard
29
374
0
03 Jun 2018
An inexact subsampled proximal Newton-type method for large-scale
  machine learning
An inexact subsampled proximal Newton-type method for large-scale machine learning
Xuanqing Liu
Cho-Jui Hsieh
J. Lee
Yuekai Sun
35
15
0
28 Aug 2017
Federated Multi-Task Learning
Federated Multi-Task Learning
Virginia Smith
Chao-Kai Chiang
Maziar Sanjabi
Ameet Talwalkar
FedML
17
1,781
0
30 May 2017
Horde of Bandits using Gaussian Markov Random Fields
Horde of Bandits using Gaussian Markov Random Fields
Sharan Vaswani
Mark W. Schmidt
L. Lakshmanan
21
14
0
07 Mar 2017
Randomized block proximal damped Newton method for composite
  self-concordant minimization
Randomized block proximal damped Newton method for composite self-concordant minimization
Zhaosong Lu
19
11
0
01 Jul 2016
Optimization Methods for Large-Scale Machine Learning
Optimization Methods for Large-Scale Machine Learning
Léon Bottou
Frank E. Curtis
J. Nocedal
102
3,176
0
15 Jun 2016
A constrained L1 minimization approach for estimating multiple Sparse
  Gaussian or Nonparanormal Graphical Models
A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models
Beilun Wang
Ritambhara Singh
Yanjun Qi
24
12
0
11 May 2016
New Optimisation Methods for Machine Learning
New Optimisation Methods for Machine Learning
Aaron Defazio
46
6
0
09 Oct 2015
High-dimensional robust precision matrix estimation: Cellwise corruption
  under $ε$-contamination
High-dimensional robust precision matrix estimation: Cellwise corruption under εεε-contamination
Po-Ling Loh
X. Tan
23
30
0
24 Sep 2015
Dropping Convexity for Faster Semi-definite Optimization
Dropping Convexity for Faster Semi-definite Optimization
Srinadh Bhojanapalli
Anastasios Kyrillidis
Sujay Sanghavi
27
172
0
14 Sep 2015
Exact Hybrid Covariance Thresholding for Joint Graphical Lasso
Exact Hybrid Covariance Thresholding for Joint Graphical Lasso
Qingming Tang
Chao Yang
Jian Peng
Jinbo Xu
28
6
0
07 Mar 2015
Large-scale randomized-coordinate descent methods with non-separable
  linear constraints
Large-scale randomized-coordinate descent methods with non-separable linear constraints
Sashank J. Reddi
Ahmed S. Hefny
Carlton Downey
Kumar Avinava Dubey
S. Sra
29
19
0
09 Sep 2014
L0 Sparse Inverse Covariance Estimation
L0 Sparse Inverse Covariance Estimation
G. Marjanovic
Alfred Hero
29
38
0
05 Aug 2014
Learning Laplacian Matrix in Smooth Graph Signal Representations
Learning Laplacian Matrix in Smooth Graph Signal Representations
Xiaowen Dong
D. Thanou
P. Frossard
P. Vandergheynst
29
28
0
30 Jun 2014
Proximal Quasi-Newton for Computationally Intensive L1-regularized
  M-estimators
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators
Kai Zhong
Ian En-Hsu Yen
Inderjit S. Dhillon
Pradeep Ravikumar
51
31
0
27 Jun 2014
Scalable sparse covariance estimation via self-concordance
Scalable sparse covariance estimation via self-concordance
Anastasios Kyrillidis
Rabeeh Karimi Mahabadi
Quoc Tran-Dinh
V. Cevher
40
13
0
13 May 2014
G-AMA: Sparse Gaussian graphical model estimation via alternating
  minimization
G-AMA: Sparse Gaussian graphical model estimation via alternating minimization
Onkar Dalal
B. Rajaratnam
22
26
0
13 May 2014
Practical Inexact Proximal Quasi-Newton Method with Global Complexity
  Analysis
Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis
K. Scheinberg
Xiaocheng Tang
39
81
0
26 Nov 2013
Composite Self-Concordant Minimization
Composite Self-Concordant Minimization
Quoc Tran-Dinh
Anastasios Kyrillidis
V. Cevher
49
94
0
13 Aug 2013
Node-Based Learning of Multiple Gaussian Graphical Models
Node-Based Learning of Multiple Gaussian Graphical Models
Karthika Mohan
Palma London
Maryam Fazel
Daniela Witten
Su-In Lee
36
206
0
21 Mar 2013
Proximal Newton-type methods for minimizing composite functions
Proximal Newton-type methods for minimizing composite functions
J. Lee
Yuekai Sun
M. Saunders
41
306
0
07 Jun 2012
Sparse Approximation via Penalty Decomposition Methods
Sparse Approximation via Penalty Decomposition Methods
Zhaosong Lu
Yong Zhang
47
208
0
10 May 2012
Convergence Properties of Kronecker Graphical Lasso Algorithms
Convergence Properties of Kronecker Graphical Lasso Algorithms
Theodoros Tsiligkaridis
Alfred Hero
Shuheng Zhou
51
76
0
03 Apr 2012
Learning a Common Substructure of Multiple Graphical Gaussian Models
Learning a Common Substructure of Multiple Graphical Gaussian Models
Satoshi Hara
Takashi Washio
CML
59
32
0
01 Mar 2012
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