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Low-Rank Matrix Completion
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Low-Rank Matrix Completion is an important problem with several applications in areas such as recommendation systems, sketching, and quantum tomography. The goal in matrix completion is to recover a low rank matrix, given a small number of entries of the matrix.
Source: Universal Matrix Completion
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AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization
Truncated Matrix Completion - An Empirical Study
Norm-Bounded Low-Rank Adaptation
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size
Low rank matrix completion and realization of graphs: results and problems
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
Abrupt Learning in Transformers: A Case Study on Matrix Completion
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks
Online Matrix Completion: A Collaborative Approach with Hott Items
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
Symmetric Matrix Completion with ReLU Sampling
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
Efficient Federated Low Rank Matrix Completion
Discrete Aware Matrix Completion via Convexized $\ell_0$-Norm Approximation
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image Inpainting
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling
Effect of Beampattern on Matrix Completion with Sparse Arrays
Linear Recursive Feature Machines provably recover low-rank matrices
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion
Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer
Matrix Completion in Almost-Verification Time
Data-based system representations from irregularly measured data
Teaching Arithmetic to Small Transformers
Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learning
Graph-Based Matrix Completion Applied to Weather Data
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation
Matrix Completion from General Deterministic Sampling Patterns
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning
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