Papers Matrix Completion
“Matrix Completion” 태그가 달린 논문 796편 · 필터 해제
Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees
This paper addresses the problem of mixed-membership estimation in networks, where the goal is to efficiently estimate the latent mixed-membership structure from the observed network. Recognizing the widespread availabil…
Matrix CompletionEfficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent
We consider the noisy matrix sensing problem in the over-parameterization setting, where the estimated rank $r$ is larger than the true rank $r_\star$ of the target matrix $X_\star$. Specifically, our main objective is t…
Matrix CompletionNorm-Bounded Low-Rank Adaptation
In this work, we propose norm-bounded low-rank adaptation (NB-LoRA) for parameter-efficient fine tuning. We introduce two parameterizations that allow explicit bounds on each singular value of the weight adaptation matri…
Low-Rank Matrix CompletionMatrix Completionparameter-efficient fine-tuningPrivacy PreservingFaster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size
Many models used in machine learning have become so large that even computer computation of the full gradient of the loss function is impractical. This has made it necessary to efficiently train models using limited avai…
Low-Rank Matrix CompletionMatrix CompletionMatrix Completion in Group Testing: Bounds and Simulations
The main goal of group testing is to identify a small number of defective items in a large population of items. A test on a subset of items is positive if the subset contains at least one defective item and negative othe…
Matrix CompletionRobust Egoistic Rigid Body Localization
We consider a robust and self-reliant (or "egoistic") variation of the rigid body localization (RBL) problem, in which a primary rigid body seeks to estimate the pose (i.e., location and orientation) of another rigid bod…
Matrix CompletionLow rank matrix completion and realization of graphs: results and problems
The Netflix problem (from machine learning) asks the following. Given a ratings matrix in which each entry $(i,j)$ represents the rating of movie $j$ by customer $i$, if customer $i$ has watched movie $j$, and is otherwi…
Low-Rank Matrix CompletionMatrix CompletionImproved Approximation Algorithms for Low-Rank Problems Using Semidefinite Optimization
Inspired by the impact of the Goemans-Williamson algorithm on combinatorial optimization, we construct an analogous relax-then-sample strategy for low-rank optimization problems. First, for orthogonally constrained quadr…
Combinatorial OptimizationMatrix CompletionDeeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery
Robust matrix completion (RMC) is a widely used machine learning tool that simultaneously tackles two critical issues in low-rank data analysis: missing data entries and extreme outliers. This paper proposes a novel scal…
Cloud RemovalMatrix CompletionVideo Background SubtractionRobust Matrix Completion for Discrete Rating-Scale Data
Matrix completion has gained considerable interest in recent years. The goal of matrix completion is to predict the unknown entries of a partially observed matrix using its known entries. Although common applications fea…
Matrix CompletionRecommendation SystemsMatrix Completion via Residual Spectral Matching
Noisy matrix completion has attracted significant attention due to its applications in recommendation systems, signal processing and image restoration. Most existing works rely on (weighted) least squares methods under v…
Image RestorationMatrix CompletionRecommendation SystemsRepresentational Transfer Learning for Matrix Completion
We propose to transfer representational knowledge from multiple sources to a target noisy matrix completion task by aggregating singular subspaces information. Under our representational similarity framework, we first in…
Matrix CompletionTransfer LearningA privacy-preserving distributed credible evidence fusion algorithm for collective decision-making
The theory of evidence reasoning has been applied to collective decision-making in recent years. However, existing distributed evidence fusion methods lead to participants' preference leakage and fusion failures as they …
Decision MakingLow-Rank Matrix CompletionMatrix CompletionPrivacy PreservingOn adaptivity and minimax optimality of two-sided nearest neighbors
Nearest neighbor (NN) algorithms have been extensively used for missing data problems in recommender systems and sequential decision-making systems. Prior theoretical analysis has established favorable guarantees for NN …
Decision MakingMatrix CompletionRecommendation SystemsSequential Decision MakingEfficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose an efficient and robust low-rank model …
Low-Rank Matrix CompletionMatrix CompletionState EstimationMulti-Channel Hypergraph Contrastive Learning for Matrix Completion
Rating is a typical user explicit feedback that visually reflects how much a user likes a related item. The (rating) matrix completion is essentially a rating prediction process, which is also a significant problem in re…
Contrastive LearningHypergraph Contrastive LearningMatrix CompletionRecommendation SystemsAbrupt Learning in Transformers: A Case Study on Matrix Completion
Recent analysis on the training dynamics of Transformers has unveiled an interesting characteristic: the training loss plateaus for a significant number of training steps, and then suddenly (and sharply) drops to near--o…
Language ModelingLanguage ModellingLow-Rank Matrix CompletionMasked Language Modeling+1Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program
Mobile health (mHealth) programs face a critical challenge in optimizing the timing of automated health information calls to beneficiaries. This challenge has been formulated as a collaborative multi-armed bandit problem…
Matrix CompletionThompson SamplingLow-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds
We present a new sampling-based approach for enabling efficient computation of low-rank Bayesian matrix completion and quantifying the associated uncertainty. Firstly, we design a new prior model based on the singular-va…
Matrix CompletionLearning Counterfactual Distributions via Kernel Nearest Neighbors
Consider a setting with multiple units (e.g., individuals, cohorts, geographic locations) and outcomes (e.g., treatments, times, items), where the goal is to learn a multivariate distribution for each unit-outcome entry,…
counterfactualMatrix Completion