Papers Matrix Completion
“Matrix Completion” 태그가 달린 논문 796편 · 필터 해제
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space
We study the problem of distributional matrix completion: Given a sparsely observed matrix of empirical distributions, we seek to impute the true distributions associated with both observed and unobserved matrix entries.…
Matrix CompletionCan We Predict Performance of Large Models across Vision-Language Tasks?
Evaluating large vision-language models (LVLMs) is very expensive, due to the high computational costs and the wide variety of tasks. The good news is that if we already have some observed performance scores, we may be a…
Matrix CompletionRiemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees
The problem of determining the configuration of points from partial distance information, known as the Euclidean Distance Geometry (EDG) problem, is fundamental to many tasks in the applied sciences. In this paper, we pr…
Low-Rank Matrix CompletionMatrix CompletionRiemannian optimizationHierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures
Predicting the thermodynamic properties of mixtures is crucial for process design and optimization in chemical engineering. Machine learning (ML) methods are gaining increasing attention in this field, but experimental d…
ClusteringMatrix CompletionTailed Low-Rank Matrix Factorization for Similarity Matrix Completion
Similarity matrix serves as a fundamental tool at the core of numerous downstream machine-learning tasks. However, missing data is inevitable and often results in an inaccurate similarity matrix. To address this issue, S…
Matrix CompletionA Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization
We develop R2N, a modified quasi-Newton method for minimizing the sum of a $\mathcal{C}^1$ function $f$ and a lower semi-continuous prox-bounded $h$. Both $f$ and $h$ may be nonconvex. At each iteration, our method compu…
DenoisingImage DenoisingMatrix CompletionEntry-Specific Matrix Estimation under Arbitrary Sampling Patterns through the Lens of Network Flows
Matrix completion tackles the task of predicting missing values in a low-rank matrix based on a sparse set of observed entries. It is often assumed that the observation pattern is generated uniformly at random or has a v…
Matrix CompletionMissing ValuesNegative Binomial Matrix Completion
Matrix completion focuses on recovering missing or incomplete information in matrices. This problem arises in various applications, including image processing and network analysis. Previous research proposed Poisson matr…
Matrix CompletionDecentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks
This article studies the problem of decentralized Singular Value Decomposition (d-SVD), which is fundamental in various signal processing applications. Two scenarios are considered depending on the availability of the da…
Low-Rank Matrix CompletionMatrix CompletionOnline Matrix Completion: A Collaborative Approach with Hott Items
We investigate the low rank matrix completion problem in an online setting with ${M}$ users, ${N}$ items, ${T}$ rounds, and an unknown rank-$r$ reward matrix ${R}\in \mathbb{R}^{{M}\times {N}}$. This problem has been wel…
Low-Rank Matrix CompletionMatrix CompletionLeave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions
We study the problem of robust matrix completion (RMC), where the partially observed entries of an underlying low-rank matrix is corrupted by sparse noise. Existing analysis of the non-convex methods for this problem eit…
Low-Rank Matrix CompletionMatrix CompletionAdvancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0
Accurate prediction of thermodynamic properties is pivotal in chemical engineering for optimizing process efficiency and sustainability. Physical group-contribution (GC) methods are widely employed for this purpose but s…
Matrix CompletionPredictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMM
We study the problem of learning a partially observed matrix under the low rank assumption in the presence of fully observed side information that depends linearly on the true underlying matrix. This problem consists of …
DenoisingImage DenoisingMatrix CompletionRecommendation SystemsGeneralized Low-Rank Matrix Completion Model with Overlapping Group Error Representation
The low-rank matrix completion (LRMC) technology has achieved remarkable results in low-level visual tasks. There is an underlying assumption that the real-world matrix data is low-rank in LRMC. However, the real matrix …
Low-Rank Matrix CompletionMatrix CompletionThe heterogeneous impact of the EU-Canada agreement with causal machine learning
This paper introduces a causal machine learning approach to investigate the impact of the EU-Canada Comprehensive Economic Trade Agreement (CETA). We propose a matrix completion algorithm on French customs data to obtain…
Matrix CompletionAmplify Graph Learning for Recommendation via Sparsity Completion
Graph learning models have been widely deployed in collaborative filtering (CF) based recommendation systems. Due to the issue of data sparsity, the graph structure of the original input lacks potential positive preferen…
Collaborative FilteringGraph LearningGraph Neural NetworkMatrix Completion+2Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy
The sixth generation (6G) of wireless networks introduces integrated sensing and communication (ISAC), a technology in which communication and sensing functionalities are inextricably linked, sharing resources across tim…
Integrated sensing and communicationISACMatrix CompletionSchedulingDemystifying Language Model Forgetting with Low-rank Example Associations
Large Language models (LLMs) suffer from forgetting of upstream knowledge when fine-tuned. Despite efforts on mitigating forgetting, few have investigated how forgotten upstream examples are dependent on newly learned ta…
Language ModelingLanguage ModellingMatrix CompletionProximal Interacting Particle Langevin Algorithms
We introduce a class of algorithms, termed proximal interacting particle Langevin algorithms (PIPLA), for inference and learning in latent variable models whose joint probability density is non-differentiable. Leveraging…
DeblurringImage DeblurringMatrix Completionparameter estimationLearning Translations via Matrix Completion
Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora. We model this task as a matrix completion problem, and present an effective and extendable framework for completin…
Bilingual Lexicon InductionMatrix Completion