Transfer Learning in Large-Scale Gaussian Graphical Models with False Discovery Rate Control
From MaRDI portal
Publication:6077601
DOI10.1080/01621459.2022.2044333arXiv2010.11037OpenAlexW3094555844WikidataQ111520957 ScholiaQ111520957MaRDI QIDQ6077601
Unnamed Author, Hongzhe Li, Sai Li
Publication date: 18 October 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2010.11037
Related Items
Transfer learning for contextual multi-armed bandits, Transfer Learning under High-dimensional Generalized Linear Models
Cites Work
- Unnamed Item
- Sparse inverse covariance estimation with the graphical lasso
- Asymptotic normality and optimalities in estimation of large Gaussian graphical models
- Gaussian graphical model estimation with false discovery rate control
- Estimating sparse precision matrix: optimal rates of convergence and adaptive estimation
- Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
- TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models
- Exponential screening and optimal rates of sparse estimation
- Inferring multiple graphical structures
- Sparsistency and rates of convergence in large covariance matrix estimation
- Sparse permutation invariant covariance estimation
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- A no-free-lunch theorem for multitask learning
- Optimal learning with \textit{Q}-aggregation
- Transfer learning for nonparametric classification: minimax rate and adaptive classifier
- Joint estimation of multiple high-dimensional precision matrices
- Direct estimation of differential networks
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Joint estimation of multiple graphical models
- Model selection and estimation in the Gaussian graphical model
- Semisupervised Inference for Explained Variance in High Dimensional Linear Regression and its Applications
- The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
- Testing differential networks with applications to the detection of gene-gene interactions
- Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation