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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 76–100 of 796 papers

TitleStatusHype
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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