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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 201–250 of 796 papers

TitleStatusHype
Adaptive Noisy Matrix Completion—0
Lifelong Matrix Completion with Sparsity-Number—0
Flat minima generalize for low-rank matrix recovery—0
Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning—0
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex MinimizationCode0
High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantization—0
Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects—0
Indiscriminate Poisoning Attacks on Unsupervised Contrastive LearningCode1
Two-snapshot DOA Estimation via Hankel-structured Matrix Completion—0
Splitting numerical integration for matrix completion—0
Counterfactual inference for sequential experiments—0
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss—0
Inductive Matrix Completion: No Bad Local Minima and a Fast AlgorithmCode0
LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion—0
Dense Air Quality Maps Using Regressive Facility Location Based Drive By Sensing—0
Variational Bayesian Filtering with Subspace Information for Extreme Spatio-Temporal Matrix Completion—0
Matrix Completion with Hierarchical Graph Side Information—0
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
Fine-grained Generalization Analysis of Inductive Matrix Completion—0
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A BenchmarkCode1
PAC-Bayesian matrix completion with a spectral scaled Student prior—0
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
Nonnegative Tensor Completion via Integer OptimizationCode0
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers—0
WARPd: A linearly convergent first-order method for inverse problems with approximate sharpness conditionsCode0
Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise—0
Projection-Free Algorithm for Stochastic Bi-level Optimization—0
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders—0
Computational Graph Completion—0
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods—0
AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix CompletionCode1
One-Bit Matrix Completion with Differential Privacy—0
Causal Matrix CompletionCode1
Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization—0
Provable Low Rank Plus Sparse Matrix Separation Via Nonconvex Regularizers—0
Weighted Low Rank Matrix Approximation and Acceleration—0
Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation—0
On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs—0
Matrix Completion of World Trade—0
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization—0
GLocal-K: Global and Local Kernels for Recommender SystemsCode1
Inductive Matrix Completion Using Graph AutoencoderCode1
Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural NetworksCode1
Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data—0
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates—0
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation—0
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization—0
GNMR: A provable one-line algorithm for low rank matrix recoveryCode0
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