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Low-Rank Matrix Completion

Low-Rank Matrix Completion is an important problem with several applications in areas such as recommendation systems, sketching, and quantum tomography. The goal in matrix completion is to recover a low rank matrix, given a small number of entries of the matrix.

Source: Universal Matrix Completion

Papers

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Showing 76–100 of 158 papers

TitleStatusHype
Matrix Completion in Almost-Verification Time—0
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes—0
Nearly-optimal Robust Matrix Completion—0
Nearly Optimal Robust Matrix Completion—0
New Hardness Results for Low-Rank Matrix Completion—0
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization—0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data—0
Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learning—0
Norm-Bounded Low-Rank Adaptation—0
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series—0
Nuclear norm penalization and optimal rates for noisy low rank matrix completion—0
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion—0
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion—0
Online Low Rank Matrix Completion—0
Online Matrix Completion: A Collaborative Approach with Hott Items—0
Online Matrix Completion and Online Robust PCA—0
Disjunctive Branch-And-Bound for Certifiably Optimal Low-Rank Matrix Completion—0
Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser—0
Phase transitions and sample complexity in Bayes-optimal matrix factorization—0
Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization—0
Probabilistic low-rank matrix completion on finite alphabets—0
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms—0
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion—0
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion—0
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders—0
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