Pages that link to "Item:Q1928276"
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The following pages link to The convex geometry of linear inverse problems (Q1928276):
Displaying 50 items.
- Hierarchical isometry properties of hierarchical measurements (Q2118397) (← links)
- Fundamental barriers to high-dimensional regression with convex penalties (Q2119224) (← links)
- Noisy tensor completion via the sum-of-squares hierarchy (Q2144539) (← links)
- The restricted isometry property of block diagonal matrices for group-sparse signal recovery (Q2155809) (← links)
- Biorthogonal greedy algorithms in convex optimization (Q2155817) (← links)
- Screening for a reweighted penalized conditional gradient method (Q2165597) (← links)
- Generalizing CoSaMP to signals from a union of low dimensional linear subspaces (Q2175016) (← links)
- Blind three dimensional deconvolution via convex optimization (Q2192919) (← links)
- Lasso guarantees for \(\beta \)-mixing heavy-tailed time series (Q2196212) (← links)
- Atomic norm minimization for decomposition into complex exponentials and optimal transport in Fourier domain (Q2202787) (← links)
- Adaptive confidence sets in shape restricted regression (Q2214235) (← links)
- Complex phase retrieval from subgaussian measurements (Q2226993) (← links)
- Sparsity of solutions for variational inverse problems with finite-dimensional data (Q2278144) (← links)
- Super-resolution by means of Beurling minimal extrapolation (Q2278456) (← links)
- Superresolution 2D DOA estimation for a rectangular array via reweighted decoupled atomic norm minimization (Q2298700) (← links)
- Approximate support recovery of atomic line spectral estimation: a tale of resolution and precision (Q2300766) (← 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)
- Solving equations of random convex functions via anchored regression (Q2317380) (← links)
- Small-deviation inequalities for sums of random matrices (Q2338012) (← links)
- On model selection consistency of regularized M-estimators (Q2340872) (← links)
- Greedy approximation in convex optimization (Q2343051) (← links)
- Average-case complexity without the black swans (Q2360669) (← links)
- One condition for solution uniqueness and robustness of both \(\ell_1\)-synthesis and \(\ell_1\)-analysis minimizations (Q2374380) (← links)
- Recovery analysis for weighted \(\ell_{1}\)-minimization using the null space property (Q2397162) (← links)
- High-dimensional change-point estimation: combining filtering with convex optimization (Q2397167) (← links)
- Recovery error analysis of noisy measurement in compressed sensing (Q2398228) (← links)
- Learning with optimal interpolation norms (Q2420165) (← links)
- Phase recovery, MaxCut and complex semidefinite programming (Q2515033) (← links)
- On risk bounds in isotonic and other shape restricted regression problems (Q2515496) (← links)
- Generalized notions of sparsity and restricted isometry property. II: Applications (Q2657397) (← links)
- Almost everywhere injectivity conditions for the matrix recovery problem (Q2659725) (← links)
- When does OMP achieve exact recovery with continuous dictionaries? (Q2659744) (← links)
- \(\ell^1\)-analysis minimization and generalized (co-)sparsity: when does recovery succeed? (Q2659754) (← links)
- Convex optimization in sums of Banach spaces (Q2667036) (← links)
- Quadratic growth conditions and uniqueness of optimal solution to Lasso (Q2671439) (← links)
- Generic error bounds for the generalized Lasso with sub-exponential data (Q2675193) (← links)
- Terracini convexity (Q2687050) (← links)
- Convergence of the forward-backward algorithm: beyond the worst-case with the help of geometry (Q2687067) (← links)
- Bernstein's inverse problem in metric linear spaces. (Q2752685) (← links)
- Estimation in High Dimensions: A Geometric Perspective (Q2799917) (← links)
- Convex Recovery of a Structured Signal from Independent Random Linear Measurements (Q2799918) (← links)
- Low Complexity Regularization of Linear Inverse Problems (Q2799919) (← links)
- Book Review: A mathematical introduction to compressive sensing (Q3178754) (← links)
- An Introduction to Compressed Sensing (Q3296174) (← links)
- Learning by atomic norm regularization with polynomial kernels (Q3451221) (← links)
- Compressed Sensing, Sparse Inversion, and Model Mismatch (Q3460830) (← links)
- Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing (Q3460831) (← links)
- Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication (Q3460837) (← links)
- Cosparsity in Compressed Sensing (Q3460838) (← links)
