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covariance matrix

101 papers

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Co-occurring keywords

graphical model (864) covariance estimation (98) high-dimensional statistics (145) linear regression (282) conditional independence (113) gaussian graphical model (106) gaussian process (1200) riemannian geometry (68) principal component analysis (316) precision matrix (58)

Papers

A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models AISTATS 2016
Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums JMLR 2016
Combinatorial semi-bandit with known covariance NIPS 2016
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) ICML 2015
On Linearly Constrained Minimum Variance Beamforming JMLR 2015
Geometry-Aware Principal Component Analysis for Symmetric Positive Definite Matrices ACML 2015
Non-asymptotic Analysis of Compressive Fisher Discriminants in terms of the Effective Dimension ACML 2015
An Efficient Statistical Method for Image Noise Level Estimation ICCV 2015
Covariance shrinkage for autocorrelated data NIPS 2014
Using Projection Kurtosis Concentration Of Natural Images For Blind Noise Covariance Matrix Estimation CVPR 2014
Sparse PCA via Covariance Thresholding NIPS 2014
New Subsampling Algorithms for Fast Least Squares Regression NIPS 2013
Sparse Principal Component Analysis for High Dimensional Multivariate Time Series AISTATS 2013
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures NIPS 2013
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA NIPS 2013
Heterogeneous Auto-similarities of Characteristics (HASC): Exploiting Relational Information for Classification ICCV 2013
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses NIPS 2012
Random Design Analysis of Ridge Regression COLT 2012
Group Lasso Estimation of High-dimensional Covariance Matrices JMLR 2011
High-dimensional Covariance Estimation Based On Gaussian Graphical Models JMLR 2011
Exploiting Feature Covariance in High-Dimensional Online Learning AISTATS 2010
Tight Sample Complexity of Large-Margin Learning NIPS 2010
The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models JMLR 2009
Augmented Functional Time Series Representation and Forecasting with Gaussian Processes NIPS 2007
Differential Entropic Clustering of Multivariate Gaussians NIPS 2006