Low-Rank Matrix Completion

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27 shown of 27 papers with code (158 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method
  7 Sep 2020 2 repositories listed
  We propose an iterative algorithm for low-rank matrix completion that can be interpreted as both an iteratively reweighted least squares (IRLS) algorithm and a saddle-escaping smoothing Newton method applied to a…
• Collaborative Filtering with Graph Information: Consistency and Scalable Methods
  1 Dec 2015 2 repositories listed
  Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space.
• Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
  6 Nov 2024 1 repository listed
  Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption.
• Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
  6 Jun 2024 1 repository listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  In practice, we demonstrate the effectiveness of this approach for deep low-rank matrix completion as well as fine-tuning language models.
• Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
  5 Jun 2024 1 repository listed
  Our experimental results on machine translation tasks demonstrate that the proposed method requires 1/16 utility metric computations compared to vanilla MBR decoding while achieving equal translation quality measured by…
• Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image Inpainting
  4 Mar 2024 1 repository listed
  Our results show that CSMC provides solutions of the same quality as state-of-the-art matrix completion algorithms based on convex optimization, while achieving significant reductions in computational runtime.
• Linear Recursive Feature Machines provably recover low-rank matrices
  9 Jan 2024 1 repository listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
  A possible explanation is that common training algorithms for neural networks implicitly perform dimensionality reduction - a process called feature learning.
• Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics
  8 Nov 2023 1 repository listed
  We empirically evaluate the effectiveness of our compression technique on matrix recovery problems.
• Teaching Arithmetic to Small Transformers
  7 Jul 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Even in the complete absence of pretraining, this approach significantly and simultaneously improves accuracy, sample complexity, and convergence speed.
• Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
  4 Feb 2023 1 repository listed
  Recent research have made significant progress by adopting two insightful tensor priors, i.
• Generalized Nonconvex Approach for Low-Tubal-Rank Tensor Recovery
  4 Aug 2022 1 repository listed
  The tensor-tensor product-induced tensor nuclear norm (t-TNN) (Lu et al., 2020) minimization for low-tubal-rank tensor recovery attracts broad attention recently.
• GNMR: A provable one-line algorithm for low rank matrix recovery
  24 Jun 2021 1 repository listed
  Low rank matrix recovery problems, including matrix completion and matrix sensing, appear in a broad range of applications.
• A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
  3 Jun 2021 1 repository listed Syntology ran 0 of 5 samples · 5 unverified · 5 pointer-only (licence)
  We propose an iterative algorithm for low-rank matrix completion that can be interpreted as an iteratively reweighted least squares (IRLS) algorithm, a saddle-escaping smoothing Newton method or a variable metric…
• Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completion
  22 Mar 2021 1 repository listed
  In this paper, we study the low-rank matrix completion problem, a class of machine learning problems, that aims at the prediction of missing entries in a partially observed matrix.
• Mixed Membership Graph Clustering via Systematic Edge Query
  25 Nov 2020 1 repository listed
  This work aims at learning mixed membership of nodes using queried edges.
• Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)
  7 Dec 2019 1 repository listed
  The main challenge with this strategy is the high computational complexity of matrix completion.
• Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning
  27 Oct 2019 1 repository listed
  In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements using structured low-rank matrix…
• Adaptive Matrix Completion for the Users and the Items in Tail
  22 Apr 2019 1 repository listed
  In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches.
• Provable Subspace Tracking from Missing Data and Matrix Completion
  6 Oct 2018 1 repository listed
  In this work, we show that a simple modification of our robust ST solution also provably solves ST-miss and robust ST-miss.
• Algebraic Variety Models for High-Rank Matrix Completion
  28 Mar 2017 1 repository listed
  We consider a generalization of low-rank matrix completion to the case where the data belongs to an algebraic variety, i.
• Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport
  18 Feb 2017 1 repository listed
  In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions.
• Riemannian stochastic variance reduced gradient on Grassmann manifold
  24 May 2016 1 repository listed
  In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a compact manifold search space.
• Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient Regularization
  20 Apr 2016 1 repository listed
  The proposed low gradient regularization is integrated with the low rank regularization into the low rank low gradient approach for depth image inpainting.
• Distant Supervision for Relation Extraction with Matrix Completion
  1 Jun 2014 1 repository listed
• Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion
  4 Apr 2014 1 repository listed
  Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.
• A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion
  27 Oct 2009 1 repository listed
  We consider the problem of reconstructing a low-rank matrix from a small subset of its entries.
• Guaranteed Rank Minimization via Singular Value Projection
  30 Sep 2009 1 repository listed
  Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with many important applications in machine learning and statistics.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.