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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 81–90 of 158 papers

TitleStatusHype
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
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