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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 11–20 of 158 papers

TitleStatusHype
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks—0
Online Matrix Completion: A Collaborative Approach with Hott Items—0
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions—0
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation—0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data—0
Symmetric Matrix Completion with ReLU Sampling—0
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & AdaptationCode1
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion AlgorithmsCode2
Efficient Federated Low Rank Matrix Completion—0
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
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