scientific article; zbMATH DE number 7255095
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Publication:4969113
zbMath1498.62117MaRDI QIDQ4969113
Eugen Pircalabelu, Gerda Claeskens
Publication date: 5 October 2020
Full work available at URL: https://jmlr.csail.mit.edu/papers/v21/19-181.html
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)
Cites Work
- Unnamed Item
- A sparse conditional Gaussian graphical model for analysis of genetical genomics data
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Pseudo-likelihood methods for community detection in large sparse networks
- Sparse inverse covariance estimation with the graphical lasso
- Joint estimation of precision matrices in heterogeneous populations
- Spectral clustering and the high-dimensional stochastic blockmodel
- Covariance regularization by thresholding
- Sparsistency and rates of convergence in large covariance matrix estimation
- The cluster graphical Lasso for improved estimation of Gaussian graphical models
- On semidefinite relaxations for the block model
- The graphical lasso: new insights and alternatives
- Sparse permutation invariant covariance estimation
- Adaptive estimation in structured factor models with applications to overlapping clustering
- Network classification with applications to brain connectomics
- Consistency of spectral clustering in stochastic block models
- Robust and computationally feasible community detection in the presence of arbitrary outlier nodes
- On the global and linear convergence of the generalized alternating direction method of multipliers
- Regularized estimation of large covariance matrices
- Community detection in dense random networks
- Mixed membership stochastic blockmodels
- Joint estimation of multiple graphical models
- Shrinking characteristics of precision matrix estimators
- The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
- A useful variant of the Davis–Kahan theorem for statisticians
