scientific article
From MaRDI portal
Publication:2896128
zbMath1242.62069MaRDI QIDQ2896128
Sewoong Oh, Raghunandan H. Keshavan, Andrea Montanari
Publication date: 13 July 2012
Full work available at URL: http://www.jmlr.org/papers/v11/keshavan10a.html
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Related Items
Matrix Completion Methods for Causal Panel Data Models, Low Permutation-rank Matrices: Structural Properties and Noisy Completion, Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA, Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery, Unnamed Item, A randomised iterative method for solving factorised linear systems, Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution Under Random Designs, Operator shifting for noisy elliptic systems, Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data, Entrywise limit theorems for eigenvectors of signal-plus-noise matrix models with weak signals, Link Prediction for Egocentrically Sampled Networks, A Zero-imputation Approach in Recommendation Systems with Data Missing Heterogeneously, A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers, An optimal statistical and computational framework for generalized tensor estimation, Heteroskedastic PCA: algorithm, optimality, and applications, \(S_{1/2}\) regularization methods and fixed point algorithms for affine rank minimization problems, Convex optimization learning of faithful Euclidean distance representations in nonlinear dimensionality reduction, Optimal large-scale quantum state tomography with Pauli measurements, Role of sparsity and structure in the optimization landscape of non-convex matrix sensing, Low-Rank and Sparse Dictionary Learning, Low-Rank Transfer Learning, On the nuclear norm and the singular value decomposition of tensors, Tight risk bound for high dimensional time series completion, Matrix completion via max-norm constrained optimization, A rank-corrected procedure for matrix completion with fixed basis coefficients, Noisy tensor completion via the sum-of-squares hierarchy, Efficient preconditioning for noisy separable nonnegative matrix factorization problems by successive projection based low-rank approximations, Iterative Collaborative Filtering for Sparse Matrix Estimation, Unnamed Item, A graphical approach to the analysis of matrix completion, Deterministic algorithms for matrix completion, Unnamed Item, Alternating proximal gradient method for convex minimization, Statistically optimal and computationally efficient low rank tensor completion from noisy entries, Transfer learning in heterogeneous collaborative filtering domains, Nonconvex Low-Rank Tensor Completion from Noisy Data, Optimal prediction in the linearly transformed spiked model, Double instrumental variable estimation of interaction models with big data, Bayesian singular value regularization via a cumulative shrinkage process, An alternating minimization method for matrix completion problems, Matrix completion by singular value thresholding: sharp bounds, Empirical Bayes matrix completion, High-dimensional covariance matrix estimation with missing observations, Low-rank tensor completion by Riemannian optimization, Implicit regularization in nonconvex statistical estimation: gradient descent converges linearly for phase retrieval, matrix completion, and blind deconvolution, Random perturbation of low rank matrices: improving classical bounds, Compressed sensing and matrix completion with constant proportion of corruptions, Matrix completion with nonconvex regularization: spectral operators and scalable algorithms, Entrywise eigenvector analysis of random matrices with low expected rank, Adaptive multinomial matrix completion, Generalized transfer subspace learning through low-rank constraint, Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm, Compressive Sensing, Lower bounds for finding stationary points I, Non-asymptotic approach to varying coefficient model, Second order accurate distributed eigenvector computation for extremely large matrices, Rank penalized estimators for high-dimensional matrices, Adaptive confidence sets for matrix completion, Asymptotic equivalence of quantum state tomography and noisy matrix completion, Sampling, denoising and compression of matrices by coherent matrix organization, Noisy low-rank matrix completion with general sampling distribution, Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization, An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion, Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion, Krylov Methods for Low-Rank Regularization, Stable rank-one matrix completion is solved by the level \(2\) Lasserre relaxation, Uniform Bounds for Invariant Subspace Perturbations, Low-Rank Representation of Tensor Network Operators with Long-Range Pairwise Interactions, Noise-tolerance matrix completion for location recommendation, A modified augmented Lagrange multiplier algorithm for Toeplitz matrix completion, Bayesian matrix completion approach to causal inference with panel data, Adapting Regularized Low-Rank Models for Parallel Architectures, Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics, Max-norm optimization for robust matrix recovery, Matrix completion based on Gaussian parameterized belief propagation, Robust matrix completion, Convex low rank approximation, Convergence of fixed-point continuation algorithms for matrix rank minimization, Robust PCA and subspace tracking from incomplete observations using \(\ell _0\)-surrogates, Fixed point and Bregman iterative methods for matrix rank minimization, Two Newton methods on the manifold of fixed-rank matrices endowed with Riemannian quotient geometries, Fixed-rank matrix factorizations and Riemannian low-rank optimization, Estimation of high-dimensional low-rank matrices, Estimation of (near) low-rank matrices with noise and high-dimensional scaling, Unnamed Item, Sharp variable selection of a sparse submatrix in a high-dimensional noisy matrix, Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach, Collaborative filtering with information-rich and~information-sparse entities, Robust high-dimensional factor models with applications to statistical machine learning, Robust principal component pursuit via inexact alternating minimization on matrix manifolds, Subspace estimation from unbalanced and incomplete data matrices: \({\ell_{2,\infty}}\) statistical guarantees, Decentralized and privacy-preserving low-rank matrix completion, Linear Models Based on Noisy Data and the Frisch Scheme, Majorized proximal alternating imputation for regularized rank constrained matrix completion, Guarantees of Riemannian Optimization for Low Rank Matrix Recovery, Optspace, Spectral method and regularized MLE are both optimal for top-\(K\) ranking, Riemannian Optimization for High-Dimensional Tensor Completion, Online Collaborative Filtering on Graphs, Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence, Unnamed Item, Gradient Descent Only Converges to Minimizers: Non-Isolated Critical Points and Invariant Regions, Unnamed Item, Structured matrix estimation and completion, On relaxed greedy randomized iterative methods for the solution of factorized linear systems, On the geometric analysis of a quartic-quadratic optimization problem under a spherical constraint, Matrix estimation by universal singular value thresholding, Unnamed Item, Iterative Methods for Solving Factorized Linear Systems, Unnamed Item, Proof methods for robust low-rank matrix recovery, Low-rank matrix completion via preconditioned optimization on the Grassmann manifold, Distributed user profiling via spectral methods
