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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 101–110 of 158 papers

TitleStatusHype
Matrix Completion from General Deterministic Sampling Patterns—0
Matrix Completion from Power-Law Distributed Samples—0
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
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