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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 1–25 of 158 papers

TitleStatusHype
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion AlgorithmsCode2
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & AdaptationCode1
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image InpaintingCode1
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Teaching Arithmetic to Small TransformersCode1
Guaranteed Tensor Recovery Fused Low-rankness and SmoothnessCode1
Generalized Nonconvex Approach for Low-Tubal-Rank Tensor RecoveryCode1
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order MethodCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
New Hardness Results for Low-Rank Matrix Completion—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
Truncated Matrix Completion - An Empirical Study—0
Norm-Bounded Low-Rank Adaptation—0
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size—0
Low rank matrix completion and realization of graphs: results and problems—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory DataCode0
Abrupt Learning in Transformers: A Case Study on Matrix Completion—0
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees—0
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
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