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Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

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Showing 51–60 of 796 papers

Title	Date	Tasks	Status	Score
Doubly robust nearest neighbors in factor models	Nov 25, 2022	counterfactualCounterfactual Inference	CodeCode Available	5
A Neural Network for Semigroups	Mar 12, 2021	DenoisingImage Reconstruction	CodeCode Available	5
Adversarially-Trained Nonnegative Matrix Factorization	Apr 10, 2021	Dimensionality ReductionMatrix Completion	CodeCode Available	5
Dictionary Learning for Massive Matrix Factorization	May 3, 2016	Collaborative FilteringDictionary Learning	CodeCode Available	5
Fast Low-Rank Matrix Learning with Nonconvex Regularization	Dec 3, 2015	Matrix Completion	CodeCode Available	5
Counterfactual inference for sequential experiments	Feb 14, 2022	counterfactualCounterfactual Inference	CodeCode Available	5
Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA	Sep 4, 2020	Collaborative FilteringMatrix Completion	CodeCode Available	5
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function	Mar 31, 2025	Data CompressionMath	CodeCode Available	5
An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization	Oct 23, 2020	Matrix Completion	CodeCode Available	5
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation	Jun 12, 2025	Contrastive LearningDenoising	CodeCode Available	5

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