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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 401–450 of 796 papers

TitleStatusHype
Matrix Completion in the Unit Hypercube via Structured Matrix FactorizationCode0
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion—0
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender SystemsCode0
Collaborative Self-Attention for Recommender Systems—0
Prediction with Unpredictable Feature Evolution—0
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers—0
Adaptive Matrix Completion for the Users and the Items in TailCode0
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence—0
On Low-rank Trace Regression under General Sampling DistributionCode0
Matrix Completion With Selective Sampling—0
Binary matrix completion with nonconvex regularizers—0
Multi-View Matrix Completion for Multi-Label Image Classification—0
Cluster Developing 1-Bit Matrix Completion—0
Machine Learning Methods Economists Should Know About—0
Ensemble Methods for Causal Effects in Panel Data Settings—0
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual PredictionCode0
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction—0
Provable Tensor Ring CompletionCode0
Matrix Completion via Nonconvex Regularization: Convergence of the Proximal Gradient AlgorithmCode0
On the convex geometry of blind deconvolution and matrix completion—0
Multispectral snapshot demosaicing via non-convex matrix completion—0
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization—0
Provable Low Rank Phase RetrievalCode0
Robust Matrix Completion State Estimation in Distribution Systems—0
Geometric Matrix Completion with Deep Conditional Random Fields—0
Computing large market equilibria using abstractions—0
Nonconvex Rectangular Matrix Completion via Gradient Descent without _2, Regularization—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Imputation and low-rank estimation with Missing Not At Random dataCode0
Matrix Completion under Low-Rank Missing Mechanism—0
Interpretable Matrix Completion: A Discrete Optimization Approach—0
Matrix Factorization via Deep Learning—0
Mixture Matrix Completion—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
Bayesian graph convolutional neural networks for semi-supervised classificationCode0
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference—0
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Scalable Recommender Systemsthrough Recursive Evidence Chains—0
Faster Matrix Completion Using Randomized SVDCode0
Provable Subspace Tracking from Missing Data and Matrix CompletionCode0
Training Complex Models with Multi-Task Weak SupervisionCode0
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview—0
Modeling longitudinal data using matrix completionCode0
Matrix Completion with Weighted Constraint for Haplotype Estimation—0
Clipped Matrix Completion: A Remedy for Ceiling Effects—0
Multi-Target Prediction: A Unifying View on Problems and Methods—0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
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