Pages that link to "Item:Q661157"

The following pages link to Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion (Q661157):

Displaying 50 items.

• Slope meets Lasso: improved oracle bounds and optimality (Q1990596) (← links)
• Sub-Gaussian estimators of the mean of a random matrix with heavy-tailed entries (Q1991680) (← links)
• Towards optimal estimation of bivariate isotonic matrices with unknown permutations (Q1996765) (← links)
• Matrix factorization for multivariate time series analysis (Q2008611) (← links)
• Parametric and semiparametric reduced-rank regression with flexible sparsity (Q2018603) (← links)
• Double fused Lasso regularized regression with both matrix and vector valued predictors (Q2044365) (← links)
• Regularization parameter selection for the low rank matrix recovery (Q2046538) (← links)
• Bridging convex and nonconvex optimization in robust PCA: noise, outliers and missing data (Q2054540) (← links)
• Data fusion using factor analysis and low-rank matrix completion (Q2058799) (← links)
• Spectral thresholding for the estimation of Markov chain transition operators (Q2074325) (← links)
• On relaxed greedy randomized iterative methods for the solution of factorized linear systems (Q2083243) (← links)
• Conditional rotation between forecasting models (Q2106365) (← links)
• Proof methods for robust low-rank matrix recovery (Q2106469) (← links)
• An optimal statistical and computational framework for generalized tensor estimation (Q2119217) (← links)
• Inference for low-rank tensors -- no need to debias (Q2131273) (← links)
• Aggregated hold out for sparse linear regression with a robust loss function (Q2136632) (← links)
• Tight risk bound for high dimensional time series completion (Q2137821) (← links)
• How can we identify the sparsity structure pattern of high-dimensional data: an elementary statistical analysis to interpretable machine learning (Q2170515) (← links)
• Optimal prediction in the linearly transformed spiked model (Q2176630) (← links)
• Implicit regularization in nonconvex statistical estimation: gradient descent converges linearly for phase retrieval, matrix completion, and blind deconvolution (Q2189396) (← links)
• Matrix completion with nonconvex regularization: spectral operators and scalable algorithms (Q2195855) (← links)
• High-dimensional VAR with low-rank transition (Q2195856) (← links)
• Entrywise eigenvector analysis of random matrices with low expected rank (Q2196228) (← links)
• Concentration of tempered posteriors and of their variational approximations (Q2196229) (← links)
• A multi-stage convex relaxation approach to noisy structured low-rank matrix recovery (Q2220914) (← links)
• Outlier detection in networks with missing links (Q2242182) (← links)
• One-bit tensor completion via transformed tensor singular value decomposition (Q2242530) (← links)
• Maximum likelihood estimation of sparse networks with missing observations (Q2242863) (← links)
• Learning with tensors: a framework based on convex optimization and spectral regularization (Q2251466) (← links)
• Prediction error bounds for linear regression with the TREX (Q2273161) (← links)
• Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach (Q2286374) (← links)
• Robust regression via mutivariate regression depth (Q2295029) (← links)
• Provable accelerated gradient method for nonconvex low rank optimization (Q2303662) (← links)
• Sharp oracle inequalities for low-complexity priors (Q2304249) (← links)
• Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions (Q2313281) (← links)
• Generalized high-dimensional trace regression via nuclear norm regularization (Q2323374) (← links)
• Localized Gaussian width of \(M\)-convex hulls with applications to Lasso and convex aggregation (Q2325349) (← links)
• Structured matrix estimation and completion (Q2325396) (← links)
• Nonparametric estimation of low rank matrix valued function (Q2326073) (← links)
• Doubly penalized estimation in additive regression with high-dimensional data (Q2328052) (← links)
• Rapid, robust, and reliable blind deconvolution via nonconvex optimization (Q2330939) (← links)
• ROP: matrix recovery via rank-one projections (Q2338922) (← links)
• Matrix estimation by universal singular value thresholding (Q2338924) (← links)
• A Bayesian approach for noisy matrix completion: optimal rate under general sampling distribution (Q2340879) (← links)
• Lasso and probabilistic inequalities for multivariate point processes (Q2345116) (← links)
• Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction (Q2364497) (← links)
• Ridge-type regularization method for questionnaire data analysis (Q2364728) (← links)
• General inertial proximal gradient method for a class of nonconvex nonsmooth optimization problems (Q2419543) (← links)
• Sparse covariance matrix estimation in high-dimensional deconvolution (Q2419664) (← links)
• Detection of a sparse submatrix of a high-dimensional noisy matrix (Q2435256) (← links)