Papers Matrix Completion
“Matrix Completion” 태그가 달린 논문 796편 · 필터 해제
New Hardness Results for Low-Rank Matrix Completion
The low-rank matrix completion problem asks whether a given real matrix with missing values can be completed so that the resulting matrix has low rank or is close to a low-rank matrix. The completed matrix is often requi…
Low-Rank Matrix CompletionMatrix CompletionMissing ValuesContrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
Matrix completion is a widely adopted framework in recommender systems, as predicting the missing entries in the user-item rating matrix enables a comprehensive understanding of user preferences. However, current graph n…
Contrastive LearningDenoisingGraph Neural NetworkMatrix Completion+1N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
Nearest neighbor (NN) methods have re-emerged as competitive tools for matrix completion, offering strong empirical performance and recent theoretical guarantees, including entry-wise error bounds, confidence intervals, …
BenchmarkingCausal InferenceMatrix CompletionRecommendation SystemsCovariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
This paper studies the task of estimating heterogeneous treatment effects in causal panel data models, in the presence of covariate effects. We propose a novel Covariate-Adjusted Deep Causal Learning (CoDEAL) for panel d…
counterfactualImputationMatrix CompletionOptimal Transport with Heterogeneously Missing Data
We consider the problem of solving the optimal transport problem between two empirical distributions with missing values. Our main assumption is that the data is missing completely at random (MCAR), but we allow for hete…
Matrix CompletionMissing ValuesRGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees
Recovering a low rank matrix from a subset of its entries, some of which may be corrupted, is known as the robust matrix completion (RMC) problem. Existing RMC methods have several limitations: they require a relatively …
Matrix CompletionAdaptively-weighted Nearest Neighbors for Matrix Completion
In this technical note, we introduce and analyze AWNN: an adaptively weighted nearest neighbor method for performing matrix completion. Nearest neighbor (NN) methods are widely used in missing data problems across multip…
counterfactualCounterfactual InferenceMatrix CompletionRecommendation SystemsEuclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent
This paper proposes and analyzes a gradient-type algorithm based on Burer-Monteiro factorization, called the Asymmetric Projected Gradient Descent (APGD), for reconstructing the point set configuration from partial Eucli…
LEMMAMatrix CompletionAltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization
This article describes a novel optimization solution framework, called alternating gradient descent (GD) and minimization (AltGDmin), that is useful for many problems for which alternating minimization (AltMin) is a popu…
Compressive SensingFew-Shot LearningLow-Rank Matrix CompletionMatrix Completion+1Truncated Matrix Completion - An Empirical Study
Low-rank Matrix Completion (LRMC) describes the problem where we wish to recover missing entries of partially observed low-rank matrix. Most existing matrix completion work deals with sampling procedures that are indepen…
Decision MakingLow-Rank Matrix CompletionMatrix CompletionRecommendation Systems+1Computational Efficient Informative Nonignorable Matrix Completion: A Row- and Column-Wise Matrix U-Statistic Pseudo-Likelihood Approach
In this study, we establish a unified framework to deal with the high dimensional matrix completion problem under flexible nonignorable missing mechanisms. Although the matrix completion problem has attracted much attent…
Matrix CompletionAn extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function
Nonlinear matrix decomposition (NMD) with the ReLU function, denoted ReLU-NMD, is the following problem: given a sparse, nonnegative matrix $X$ and a factorization rank $r$, identify a rank-$r$ matrix $\Theta$ such that …
Data CompressionMathMatrix CompletionDepth-Aided Color Image Inpainting in Quaternion Domain
In this paper, we propose a depth-aided color image inpainting method in the quaternion domain, called depth-aided low-rank quaternion matrix completion (D-LRQMC). In conventional quaternion-based inpainting techniques, …
Image InpaintingMatrix CompletionFast Two-photon Microscopy by Neuroimaging with Oblong Random Acquisition (NORA)
Advances in neural imaging have enabled neuroscientists to study how large neural populations conspire to produce perception, behavior and cognition. Despite many advances in optical methods, there exists a fundamental t…
AnatomyMatrix CompletionA Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems
Matrix completion is fundamental for predicting missing data with a wide range of applications in personalized healthcare, e-commerce, recommendation systems, and social network analysis. Traditional matrix completion ap…
Computational EfficiencyFederated LearningMatrix CompletionRecommendation SystemsInterference-Aware Edge Runtime Prediction with Conformal Matrix Completion
Accurately estimating workload runtime is a longstanding goal in computer systems, and plays a key role in efficient resource provisioning, latency minimization, and various other system management tasks. Runtime predict…
Edge-computingMatrix CompletionPredictionOptimal Transfer Learning for Missing Not-at-Random Matrix Completion
We study transfer learning for matrix completion in a Missing Not-at-Random (MNAR) setting that is motivated by biological problems. The target matrix $Q$ has entire rows and columns missing, making estimation impossible…
Matrix CompletionTransfer LearningRecommendations from Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization
This paper provides a theoretical analysis of a new learning problem for recommender systems where users provide feedback by comparing pairs of items instead of rating them individually. We assume that comparisons stem f…
Matrix CompletionRecommendation SystemsA Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization. While this effectively densifi…
AttributeMatrix CompletionNegationMatrix Completion with Graph Information: A Provable Nonconvex Optimization Approach
We consider the problem of matrix completion with graphs as side information depicting the interrelations between variables. The key challenge lies in leveraging the similarity structure of the graph to enhance matrix re…
Matrix Completion