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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 181–190 of 796 papers

TitleStatusHype
Adaptive and Implicit Regularization for Matrix CompletionCode1
Forecasting Algorithms for Causal Inference with Panel DataCode0
Propagation Map Reconstruction via Interpolation Assisted Matrix Completion—0
SP2: A Second Order Stochastic Polyak Method—0
A Perturbation Bound on the Subspace Estimator from Canonical ProjectionsCode0
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids—0
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network—0
MultiEarth 2022 -- The Champion Solution for the Matrix Completion Challenge via Multimodal Regression and Generation—0
Introducing the Huber mechanism for differentially private low-rank matrix completion—0
Robust Matrix Completion with Heavy-tailed Noise—0
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