covariance estimation

On the Power of Truncated SVD for General High-rank Matrix Estimation Problems NeurIPS 2017 A Bayesian method for reducing bias in neural representational similarity analysis NeurIPS 2016 A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty JMLR 2016 Data Selection for Within-Class Covariance Estimation Interspeech 2016 Improved MVDR Beamforming Using Single-Channel Mask Prediction Networks Interspeech 2016 Sparse PCA via Covariance Thresholding JMLR 2016 Exact Bias Correction and Covariance Estimation for Stereo Vision CVPR 2015 Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes NeurIPS 2015 A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation NeurIPS 2014 A Novel M-Estimator for Robust PCA JMLR 2014 Distance-Based Network Recovery under Feature Correlation NeurIPS 2014 Elementary Estimators for Sparse Covariance Matrices and other Structured Moments ICML 2014 High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models JMLR 2014 Locally Adaptive Factor Processes for Multivariate Time Series JMLR 2014 A Minimum Error Vanishing Point Detection Approach for Uncalibrated Monocular Images of Man-Made Environments CVPR 2013 Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA NeurIPS 2013 Generalizing Analytic Shrinkage for Arbitrary Covariance Structures NeurIPS 2013 Locally Adaptive Bayesian Multivariate Time Series NeurIPS 2013 Sparse Matrix Inversion with Scaled Lasso JMLR 2013 Hierarchical Latent Dictionaries for Models of Brain Activation AISTATS 2012 Multi-task Regression using Minimal Penalties JMLR 2012 On the Sample Complexity of Robust PCA NeurIPS 2012 Random Design Analysis of Ridge Regression COLT 2012 Transelliptical Component Analysis NeurIPS 2012 Group Lasso Estimation of High-dimensional Covariance Matrices JMLR 2011