precision matrix estimation

Covariance Selection over Networks AISTATS 2025 Adaptive Estimation of Graphical Models under Total Positivity ICML 2023 Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity NeurIPS 2023 MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation JMLR 2023 A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation ICML 2022 Laplacian Constrained Precision Matrix Estimation: Existence and High Dimensional Consistency AISTATS 2022 Minimax Estimation of Laplacian Constrained Precision Matrices AISTATS 2021 Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery AISTATS 2021 Community-Based Group Graphical Lasso JMLR 2020 High-dimensional Gaussian graphical models on network-linked data JMLR 2020 Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model NeurIPS 2020 Bayesian Joint Estimation of Multiple Graphical Models NeurIPS 2019 Joint Nonparametric Precision Matrix Estimation with Confounding UAI 2019 Precision Matrix Estimation with Noisy and Missing Data AISTATS 2019 Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization ICML 2018 Inverse Covariance Estimation with Structured Groups IJCAI 2017 Robust Gaussian Graphical Model Estimation with Arbitrary Corruption ICML 2017 Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP) CVPR 2016 Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates AISTATS 2016 The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R JMLR 2015 Node-Based Learning of Multiple Gaussian Graphical Models JMLR 2014 The FASTCLIME Package for Linear Programming and Large-Scale Precision Matrix Estimation in R JMLR 2014 Adaptive Sparsity in Gaussian Graphical Models ICML 2013 A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions AISTATS 2013 A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions ICML 2013