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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 51–75 of 796 papers

TitleStatusHype
Interference-Aware Edge Runtime Prediction with Conformal Matrix CompletionCode0
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion—0
Recommendations from Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization—0
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings—0
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach—0
Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees—0
Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient DescentCode0
Norm-Bounded Low-Rank Adaptation—0
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size—0
Matrix Completion in Group Testing: Bounds and Simulations—0
Robust Egoistic Rigid Body Localization—0
Low rank matrix completion and realization of graphs: results and problems—0
Improved Approximation Algorithms for Low-Rank Problems Using Semidefinite Optimization—0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
Robust Matrix Completion for Discrete Rating-Scale DataCode0
Matrix Completion via Residual Spectral Matching—0
Representational Transfer Learning for Matrix Completion—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
On adaptivity and minimax optimality of two-sided nearest neighborsCode0
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory DataCode0
Multi-Channel Hypergraph Contrastive Learning for Matrix Completion—0
Abrupt Learning in Transformers: A Case Study on Matrix Completion—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds—0
Learning Counterfactual Distributions via Kernel Nearest NeighborsCode0
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