The following pages link to (Q4864293):
Displaying 50 items.
- A unified primal dual active set algorithm for nonconvex sparse recovery (Q2038299) (← links)
- A selective overview of deep learning (Q2038303) (← links)
- Robust high-dimensional factor models with applications to statistical machine learning (Q2038305) (← links)
- Dynamic gene regulatory network reconstruction and analysis based on clinical transcriptomic data of colorectal cancer (Q2038717) (← links)
- Inverse multiobjective optimization: inferring decision criteria from data (Q2038913) (← links)
- Necessary and sufficient conditions for variable selection consistency of the Lasso in high dimensions (Q2039788) (← links)
- Inference without compatibility: using exponential weighting for inference on a parameter of a linear model (Q2040072) (← links)
- Modified forward-backward splitting method for variational inclusions (Q2040611) (← links)
- Model selection with mixed variables on the Lasso path (Q2040668) (← links)
- The horseshoe-like regularization for feature subset selection (Q2040669) (← links)
- On making valid inferences by integrating data from surveys and other sources (Q2040672) (← links)
- High-dimensional sign-constrained feature selection and grouping (Q2042289) (← links)
- Model identification and selection for single-index varying-coefficient models (Q2042522) (← links)
- Breast cancer nuclei segmentation and classification based on a deep learning approach (Q2042989) (← links)
- A Bayesian model of microbiome data for simultaneous identification of covariate associations and prediction of phenotypic outcomes (Q2044272) (← links)
- Inferring a consensus problem list using penalized multistage models for ordered data (Q2044280) (← links)
- Bi-selection in the high-dimensional additive hazards regression model (Q2044320) (← links)
- High-dimensional variable selection via low-dimensional adaptive learning (Q2044323) (← links)
- Learning sparse conditional distribution: an efficient kernel-based approach (Q2044348) (← links)
- Multicarving for high-dimensional post-selection inference (Q2044355) (← links)
- Consistent regression using data-dependent coverings (Q2044358) (← links)
- Double fused Lasso regularized regression with both matrix and vector valued predictors (Q2044365) (← links)
- Graphical-model based high dimensional generalized linear models (Q2044367) (← links)
- Uncertainty quantification for principal component regression (Q2044374) (← links)
- Iteratively reweighted \(\ell_1\)-penalized robust regression (Q2044416) (← links)
- Multivariate variable selection by means of null-beamforming (Q2044421) (← links)
- An effective procedure for feature subset selection in logistic regression based on information criteria (Q2044566) (← links)
- Fuzzy Gaussian lasso clustering with application to cancer data (Q2045680) (← links)
- Variable selection In regression models using global sensitivity analysis (Q2046061) (← links)
- Gini correlation for feature screening (Q2046243) (← links)
- An outer-inner linearization method for non-convex and nondifferentiable composite regularization problems (Q2046332) (← links)
- The de-biased group Lasso estimation for varying coefficient models (Q2046473) (← links)
- Clustering of subsample means based on pairwise L1 regularized empirical likelihood (Q2046479) (← links)
- Regularization parameter selection for the low rank matrix recovery (Q2046538) (← links)
- Level-set subdifferential error bounds and linear convergence of Bregman proximal gradient method (Q2046546) (← links)
- New inertial relaxed method for solving split feasibilities (Q2047200) (← links)
- Eigenvector-based sparse canonical correlation analysis: fast computation for estimation of multiple canonical vectors (Q2048126) (← links)
- \(\ell_{2,0}\)-norm based selection and estimation for multivariate generalized linear models (Q2048127) (← links)
- A modulus-based iterative method for sparse signal recovery (Q2048821) (← links)
- Decomposition of longitudinal deformations via Beltrami descriptors (Q2050572) (← links)
- Quantile-based portfolios: post-model-selection estimation with alternative specifications (Q2051169) (← links)
- Kernel machines for current status data (Q2051247) (← links)
- Adaptive covariate acquisition for minimizing total cost of classification (Q2051306) (← links)
- Automated data-driven selection of the hyperparameters for total-variation-based texture segmentation (Q2051545) (← links)
- Adaptive sparse group LASSO in quantile regression (Q2051571) (← links)
- Sparse principal component regression via singular value decomposition approach (Q2051586) (← links)
- On solving double direction methods for convex constrained monotone nonlinear equations with image restoration (Q2052263) (← links)
- Target redirected regression with dynamic neighborhood structure (Q2054109) (← links)
- Evaluating visual properties via robust HodgeRank (Q2054405) (← links)
- The distribution of the Lasso: uniform control over sparse balls and adaptive parameter tuning (Q2054498) (← links)
