Pages that link to "Item:Q5131966"
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The following pages link to Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization (Q5131966):
Displaying 18 items.
- A shrinkage principle for heavy-tailed data: high-dimensional robust low-rank matrix recovery (Q820791) (← links)
- A selective overview of deep learning (Q2038303) (← links)
- Subspace estimation from unbalanced and incomplete data matrices: \({\ell_{2,\infty}}\) statistical guarantees (Q2039795) (← links)
- Error bound of critical points and KL property of exponent 1/2 for squared F-norm regularized factorization (Q2052408) (← links)
- Bridging convex and nonconvex optimization in robust PCA: noise, outliers and missing data (Q2054540) (← links)
- Proof methods for robust low-rank matrix recovery (Q2106469) (← links)
- Cauchy noise loss for stochastic optimization of random matrix models via free deterministic equivalents (Q2287214) (← links)
- Asymmetry helps: eigenvalue and eigenvector analyses of asymmetrically perturbed low-rank matrices (Q2656603) (← links)
- Nonconvex Low-Rank Tensor Completion from Noisy Data (Q5080674) (← links)
- Improved Performance Guarantees for Orthogonal Group Synchronization via Generalized Power Method (Q5081100) (← links)
- GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery (Q5089733) (← links)
- A universal rank approximation method for matrix completion (Q5097888) (← links)
- (Q5159422) (← links)
- Compressed spectral screening for large-scale differential correlation analysis with application in selecting glioblastoma gene modules (Q6138655) (← links)
- Inference for low-rank models (Q6177325) (← links)
- Inference for low-rank completion without sample splitting with application to treatment effect estimation (Q6199626) (← links)
- Inference for heteroskedastic PCA with missing data (Q6550970) (← links)
- A new complexity metric for nonconvex rank-one generalized matrix completion (Q6608034) (← links)
