The following pages link to (Q4864293):
Displaying 50 items.
- Majorization-minimization algorithms for nonsmoothly penalized objective functions (Q1952099) (← links)
- PAC-Bayesian bounds for sparse regression estimation with exponential weights (Q1952177) (← links)
- Automatic grouping using smooth-threshold estimating equations (Q1952187) (← links)
- The Lasso as an \(\ell _{1}\)-ball model selection procedure (Q1952205) (← links)
- The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso) (Q1952206) (← links)
- Sparsity considerations for dependent variables (Q1952207) (← links)
- Robust regression through the Huber's criterion and adaptive lasso penalty (Q1952217) (← links)
- The smooth-Lasso and other \(\ell _{1}+\ell _{2}\)-penalized methods (Q1952223) (← links)
- Penalized wavelets: embedding wavelets into semiparametric regression (Q1952243) (← links)
- Distributional results for thresholding estimators in high-dimensional Gaussian regression models (Q1952253) (← links)
- Least squares after model selection in high-dimensional sparse models (Q1952433) (← links)
- Sign-constrained least squares estimation for high-dimensional regression (Q1954143) (← links)
- Sparse signal recovery via ECME thresholding pursuits (Q1954822) (← links)
- A fault prognosis strategy based on time-delayed digraph model and principal component analysis (Q1955372) (← links)
- Bridge estimation for generalized linear models with a diverging number of parameters (Q1957148) (← links)
- Penalized maximum likelihood estimation of a stochastic multivariate regression model (Q1957159) (← links)
- Convergence analysis of sparse LMS algorithms with \(l_{1}\)-norm penalty based on white input signal (Q1957936) (← links)
- Decomposing the tensor kernel support vector machine for neuroscience data with structured labels (Q1959560) (← links)
- Composite kernel learning (Q1959567) (← links)
- Large scale image annotation: learning to rank with joint word-image embeddings (Q1959605) (← links)
- gBoost: a mathematical programming approach to graph classification and regression (Q1959643) (← links)
- Quantitative integration of radiomic and genomic data improves survival prediction of low-grade glioma patients (Q1980111) (← links)
- Linear embedding by joint robust discriminant analysis and inter-class sparsity (Q1982415) (← links)
- Universal sieve-based strategies for efficient estimation using machine learning tools (Q1983607) (← links)
- The Glowinski-Le Tallec splitting method revisited: a general convergence and convergence rate analysis (Q1983721) (← links)
- A partially proximal linearized alternating minimization method for finding Dantzig selectors (Q1983897) (← links)
- An alternating direction method of multipliers with the BFGS update for structured convex quadratic optimization (Q1983931) (← links)
- Inexact variable metric stochastic block-coordinate descent for regularized optimization (Q1985280) (← links)
- Generation and application of multivariate polynomial quadrature rules (Q1986197) (← links)
- A preconditioning approach for improved estimation of sparse polynomial chaos expansions (Q1986404) (← links)
- A cubic spline penalty for sparse approximation under tight frame balanced model (Q1986544) (← links)
- Sparsity-promoting elastic net method with rotations for high-dimensional nonlinear inverse problem (Q1986778) (← links)
- Robust variable selection and estimation in threshold regression model (Q1987583) (← links)
- Variable selection for varying coefficient models via kernel based regularized rank regression (Q1987596) (← links)
- Trimmed LASSO regression estimator for binary response data (Q1987665) (← links)
- Hyper nonlocal priors for variable selection in generalized linear models (Q1987723) (← links)
- Development of \(hp\)-inverse model by using generalized polynomial chaos (Q1987788) (← links)
- Pairwise fusion approach incorporating prior constraint information (Q1988277) (← links)
- ADMM-softmax: an ADMM approach for multinomial logistic regression (Q1988494) (← links)
- Condition estimation for regression and feature selection (Q1989165) (← links)
- Efficient estimates in regression models with highly correlated covariates (Q1989198) (← links)
- Model-free feature screening for high-dimensional survival data (Q1989891) (← links)
- Approximate \(\ell_0\)-penalized estimation of piecewise-constant signals on graphs (Q1990576) (← links)
- Robust low-rank matrix estimation (Q1990590) (← links)
- A comparative study of the leading machine learning techniques and two new optimization algorithms (Q1991232) (← links)
- Debiasing the Lasso: optimal sample size for Gaussian designs (Q1991670) (← links)
- Overcoming the limitations of phase transition by higher order analysis of regularization techniques (Q1991696) (← links)
- On the degrees of freedom of mixed matrix regression (Q1993055) (← links)
- Sparse approximation of fitting surface by elastic net (Q1993576) (← links)
- Usage of the GO estimator in high dimensional linear models (Q1995832) (← links)
