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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
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order MethodCode1
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image InpaintingCode1
Generalized Nonconvex Approach for Low-Tubal-Rank Tensor RecoveryCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & AdaptationCode1
Guaranteed Tensor Recovery Fused Low-rankness and SmoothnessCode1
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
Teaching Arithmetic to Small TransformersCode1
A divide-and-conquer algorithm for binary matrix completion—0
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
Bayesian Learning for Low-Rank matrix reconstruction—0
Adaptive Noisy Matrix Completion—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Abrupt Learning in Transformers: A Case Study on Matrix Completion—0
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