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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 51–100 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 neighbors—0
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
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space—0
Learning Counterfactual Distributions via Kernel Nearest Neighbors—0
Can We Predict Performance of Large Models across Vision-Language Tasks?Code0
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees—0
Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures—0
Tailed Low-Rank Matrix Factorization for Similarity Matrix Completion—0
A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization—0
Entry-Specific Matrix Estimation under Arbitrary Sampling Patterns through the Lens of Network Flows—0
Negative Binomial Matrix Completion—0
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks—0
Online Matrix Completion: A Collaborative Approach with Hott Items—0
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions—0
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0—0
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMMCode0
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation—0
The heterogeneous impact of the EU-Canada agreement with causal machine learning—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy—0
Proximal Interacting Particle Langevin AlgorithmsCode0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
Learning Translations via Matrix Completion—0
Learning Iterative Reasoning through Energy Diffusion—0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data—0
Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical PropertiesCode0
Symmetric Matrix Completion with ReLU Sampling—0
Structured Learning of Compositional Sequential InterventionsCode0
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