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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
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
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
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image InpaintingCode1
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning DynamicsCode0
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