Statistics

Dirty Statistical Models NeurIPS 2013 Efficient Learning of Simplices COLT 2013 Estimating LASSO Risk and Noise Level NeurIPS 2013 Estimating the Unseen: Improved Estimators for Entropy and other Properties NeurIPS 2013 Estimating Unknown Sparsity in Compressed Sensing ICML 2013 Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis NeurIPS 2013 Forecastable Component Analysis ICML 2013 Generalizing Analytic Shrinkage for Arbitrary Covariance Structures NeurIPS 2013 High-dimensional Inference via Lipschitz Sparsity-Yielding Regularizers AISTATS 2013 Inverse Density as an Inverse Problem: the Fredholm Equation Approach NeurIPS 2013 Learning Heteroscedastic Models by Convex Programming under Group Sparsity ICML 2013 Memory Limited, Streaming PCA NeurIPS 2013 On model selection consistency of penalized M-estimators: a geometric theory NeurIPS 2013 On the Convergence of Maximum Variance Unfolding JMLR 2013 PAC-Bayes-Empirical-Bernstein Inequality NeurIPS 2013 Parallel Transport of Deformations in Shape Space of Elastic Surfaces ICCV 2013 Passive Learning with Target Risk COLT 2013 PC Algorithm for Nonparanormal Graphical Models JMLR 2013 Precision-recall space to correct external indices for biclustering ICML 2013 Principal Component Analysis on non-Gaussian Dependent Data ICML 2013 Recursive Karcher Expectation Estimators And Geometric Law of Large Numbers AISTATS 2013 Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel NeurIPS 2013 Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model NeurIPS 2013 Scale Invariant Conditional Dependence Measures ICML 2013 Sparse Inverse Covariance Estimation with Calibration NeurIPS 2013