high-dimensional statistics

Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso NeurIPS 2015 Streaming Sparse Principal Component Analysis ICML 2015 Sum-of-Squares Lower Bounds for Sparse PCA NeurIPS 2015 Average Case Analysis of High-Dimensional Block-Sparse Recovery and Regression for Arbitrary Designs AISTATS 2014 Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings NeurIPS 2014 Covariance shrinkage for autocorrelated data NeurIPS 2014 Elementary Estimators for Graphical Models NeurIPS 2014 Elementary Estimators for Sparse Covariance Matrices and other Structured Moments ICML 2014 Exact Post Model Selection Inference for Marginal Screening NeurIPS 2014 Generalized Dantzig Selector: Application to the k-support norm NeurIPS 2014 High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models JMLR 2014 High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation JMLR 2014 Learning Graphs with a Few Hubs ICML 2014 Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression AISTATS 2014 Sparse PCA via Covariance Thresholding NeurIPS 2014 Sparse PCA with Oracle Property NeurIPS 2014 Sparsity and the Truncated l^2-norm AISTATS 2014 Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting ICML 2014 A Comparative Framework for Preconditioned Lasso Algorithms NeurIPS 2013 BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables NeurIPS 2013 Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models NeurIPS 2013 Dirty Statistical Models NeurIPS 2013 Estimating LASSO Risk and Noise Level NeurIPS 2013 Large Scale Distributed Sparse Precision Estimation NeurIPS 2013 Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation NeurIPS 2013