Papers Low-Rank Matrix Completion
“Low-Rank Matrix Completion” 태그가 달린 논문 158편 · 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 ValuesAltGDmin: 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+1Norm-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 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 CompletionA 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 PreservingEfficient 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 EstimationAbrupt 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+1Riemannian 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 optimizationDecentralized 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 CompletionGeneralized 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 CompletionNonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
Many machine learning tasks, such as principal component analysis and low-rank matrix completion, give rise to manifold optimization problems. Although there is a large body of work studying the design and analysis of al…
Computational EfficiencyFederated LearningLow-Rank Matrix CompletionMatrix CompletionSymmetric Matrix Completion with ReLU Sampling
We study the problem of symmetric positive semi-definite low-rank matrix completion (MC) with deterministic entry-dependent sampling. In particular, we consider rectified linear unit (ReLU) sampling, where only positive …
Low-Rank Matrix CompletionMatrix CompletionCompressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
While overparameterization in machine learning models offers great benefits in terms of optimization and generalization, it also leads to increased computational requirements as model sizes grow. In this work, we show th…
Language ModellingLow-Rank Matrix CompletionMatrix CompletionEfficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
Minimum Bayes Risk (MBR) decoding is a powerful decoding strategy widely used for text generation tasks, but its quadratic computational complexity limits its practical application. This paper presents a novel approach f…
Low-Rank Matrix CompletionMachine TranslationMatrix CompletionText Generation+1Efficient Federated Low Rank Matrix Completion
In this work, we develop and analyze a Gradient Descent (GD) based solution, called Alternating GD and Minimization (AltGDmin), for efficiently solving the low rank matrix completion (LRMC) in a federated setting. LRMC i…
Low-Rank Matrix CompletionMatrix CompletionDiscrete Aware Matrix Completion via Convexized $\ell_0$-Norm Approximation
We consider a novel algorithm, for the completion of partially observed low-rank matrices in a structured setting where each entry can be chosen from a finite discrete alphabet set, such as in common recommender systems.…
Low-Rank Matrix CompletionMatrix CompletionRecommendation Systems