Pages that link to "Item:Q2933860"

The following pages link to Nonparametric sparsity and regularization (Q2933860):

Displaying 50 items.

• Sparsity information and regularization in the horseshoe and other shrinkage priors (Q86320) (← links)
• Regularity properties for sparse regression (Q279682) (← links)
• Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization (Q391843) (← links)
• Proximal methods for the latent group lasso penalty (Q457209) (← links)
• Kernel based approaches to local nonlinear non-parametric variable selection (Q463794) (← links)
• Sparse and nonnegative sparse D-MORPH regression (Q498497) (← links)
• A classification-oriented dictionary learning model: explicitly learning the particularity and commonality across categories (Q898347) (← links)
• Sparse representation based Fisher discrimination dictionary learning for image classification (Q901802) (← links)
• Non-linear dictionary learning with partially labeled data (Q1669609) (← links)
• Variable selection of high-dimensional non-parametric nonlinear systems by derivative averaging to avoid the curse of dimensionality (Q1737706) (← links)
• Ranking the importance of variables in nonlinear system identification (Q1737872) (← links)
• Regularizers for structured sparsity (Q1949299) (← links)
• Convergence of stochastic proximal gradient algorithm (Q2019902) (← links)
• Statistical modeling of longitudinal data with non-ignorable non-monotone missingness with semiparametric Bayesian and machine learning components (Q2040667) (← links)
• Learning sparse conditional distribution: an efficient kernel-based approach (Q2044348) (← links)
• Improved spectral convergence rates for graph Laplacians on \(\varepsilon \)-graphs and \(k\)-NN graphs (Q2155800) (← links)
• Nonconvex regularization for sparse neural networks (Q2168678) (← links)
• Kernel variable selection for multicategory support vector machines (Q2237819) (← links)
• A unified penalized method for sparse additive quantile models: an RKHS approach (Q2409400) (← links)
• A random block-coordinate Douglas-Rachford splitting method with low computational complexity for binary logistic regression (Q2419533) (← links)
• The convergence rate of semi-supervised regression with quadratic loss (Q2423028) (← links)
• Statistical inference in compound functional models (Q2447291) (← links)
• Variable selection based on squared derivative averages (Q2664212) (← links)
• Sparsity-enforcing regularisation and ISTA revisited (Q2832548) (← links)
• The performance of semi-supervised Laplacian regularized regression with the least square loss (Q2980112) (← links)
• Bayesian Approximate Kernel Regression With Variable Selection (Q3121562) (← links)
• Norm sensitivity of sparsity regularization with respect to <i>p</i> (Q3144017) (← links)
• Low-Rank and Sparse Dictionary Learning (Q3449318) (← links)
• (Q4558476) (← links)
• Statistical sparsity (Q4562729) (← links)
• A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary (Q4616947) (← links)
• Sparse Signal Approximation via Nonseparable Regularization (Q4620785) (← links)
• Sparse Regularization via Convex Analysis (Q4621829) (← links)
• Robust and discriminative dictionary learning for face recognition (Q4634909) (← links)
• (Q4969263) (← links)
• Isotropic non-Lipschitz regularization for sparse representations of random fields on the sphere (Q5018371) (← links)
• Proximal Gradient Methods for Machine Learning and Imaging (Q5028165) (← links)
• A Maximum Principle Argument for the Uniform Convergence of Graph Laplacian Regressors (Q5037572) (← links)
• Lipschitz Regularity of Graph Laplacians on Random Data Clouds (Q5037712) (← links)
• Efficient kernel-based variable selection with sparsistency (Q5037806) (← links)
• Improvement on LASSO-type estimator in nonparametric regression (Q5051335) (← links)
• Thresholding gradient methods in Hilbert spaces: support identification and linear convergence (Q5109200) (← links)
• Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model (Q5146054) (← links)
• Variable Selection for Nonparametric Learning with Power Series Kernels (Q5214363) (← links)
• Performance analysis of the LapRSSLG algorithm in learning theory (Q5220067) (← links)
• ADMM Algorithmic Regularization Paths for Sparse Statistical Machine Learning (Q5350485) (← links)
• A General Framework of Nonparametric Feature Selection in High-Dimensional Data (Q6079788) (← links)
• The Geometry of Sparse Analysis Regularization (Q6158006) (← links)
• Structure learning via unstructured kernel-based M-estimation (Q6184881) (← links)
• High-dimensional local linear regression under sparsity and convex losses (Q6200896) (← links)