high-dimensional estimation

Simultaneous Clustering and Estimation of Heterogeneous Graphical Models JMLR 2018 A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models AISTATS 2017 Generalized Pseudolikelihood Methods for Inverse Covariance Estimation AISTATS 2017 On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models NeurIPS 2017 Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems NeurIPS 2017 Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity ICML 2017 Estimating Structured Vector Autoregressive Models ICML 2016 Joint Structural Estimation of Multiple Graphical Models JMLR 2016 Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs NeurIPS 2015 Closed-form Estimators for High-dimensional Generalized Linear Models NeurIPS 2015 Concave Penalized Estimation of Sparse Gaussian Bayesian Networks JMLR 2015 Regularized M-estimators with Nonconvexity: Statistical and Algorithmic Theory for Local Optima JMLR 2015 Covariance shrinkage for autocorrelated data NeurIPS 2014 Generalized Dantzig Selector: Application to the k-support norm NeurIPS 2014 Learning Latent Variable Gaussian Graphical Models ICML 2014 Node-Based Learning of Multiple Gaussian Graphical Models JMLR 2014 BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables NeurIPS 2013 Large Scale Distributed Sparse Precision Estimation NeurIPS 2013 Sparse Inverse Covariance Estimation with Calibration NeurIPS 2013 When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements NeurIPS 2013 Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming JMLR 2012 Structured Learning of Gaussian Graphical Models NeurIPS 2012 Transelliptical Graphical Models NeurIPS 2012 A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers NeurIPS 2009 High-Dimensional Graphical Model Selection Using ℓ_1-Regularized Logistic Regression NeurIPS 2006