Papers Matrix Completion
“Matrix Completion” 태그가 달린 논문 796편 · 필터 해제
Learning Iterative Reasoning through Energy Diffusion
We introduce iterative reasoning through energy diffusion (IRED), a novel framework for learning to reason for a variety of tasks by formulating reasoning and decision-making problems with energy-based optimization. IRED…
Decision MakingMatrix 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 CompletionBalancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties
Predicting the physico-chemical properties of pure substances and mixtures is a central task in thermodynamics. Established prediction methods range from fully physics-based ab-initio calculations, which are only feasibl…
Matrix CompletionRepresentation LearningStructured Learning of Compositional Sequential Interventions
We consider sequential treatment regimes where each unit is exposed to combinations of interventions over time. When interventions are described by qualitative labels, such as "close schools for a month due to a pandemic…
Matrix 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 CompletionConvergence of the majorized PAM method with subspace correction for low-rank composite factorization model
This paper focuses on the convergence certificates of the majorized proximal alternating minimization (PAM) method with subspace correction, proposed in \cite{TaoQianPan22} for the column $\ell_{2,0}$-norm regularized fa…
Matrix 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+1Matrix Low-Rank Approximation For Policy Gradient Methods
Estimating a policy that maps states to actions is a central problem in reinforcement learning. Traditionally, policies are inferred from the so called value functions (VFs), but exact VF computation suffers from the cur…
Matrix CompletionPolicy Gradient MethodsMatrix Low-Rank Trust Region Policy Optimization
Most methods in reinforcement learning use a Policy Gradient (PG) approach to learn a parametric stochastic policy that maps states to actions. The standard approach is to implement such a mapping via a neural network (N…
Matrix CompletionConnectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion
Matrix factorization models have been extensively studied as a valuable test-bed for understanding the implicit biases of overparameterized models. Although both low nuclear norm and low rank regularization have been stu…
Matrix CompletionThe radius of statistical efficiency
Classical results in asymptotic statistics show that the Fisher information matrix controls the difficulty of estimating a statistical model from observed data. In this work, we introduce a companion measure of robustnes…
Matrix CompletionRetrievalSubspace-Informed Matrix Completion
In this work, we consider the matrix completion problem, where the objective is to reconstruct a low-rank matrix from a few observed entries. A commonly employed approach involves nuclear norm minimization. For this meth…
Collaborative FilteringMatrix CompletionEfficient 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 SystemsOnline Policy Learning and Inference by Matrix Completion
Is it possible to make online decisions when personalized covariates are unavailable? We take a collaborative-filtering approach for decision-making based on collective preferences. By assuming low-dimensional latent fea…
Collaborative FilteringDecision MakingMatrix Completionparameter estimationStructured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems
We develop a conformal inference method to construct a joint confidence region for a given group of missing entries within a sparsely observed matrix, focusing primarily on entries from the same column. Our method is mod…
Collaborative FilteringDecision MakingMatrix CompletionRecommendation SystemsConcentration properties of fractional posterior in 1-bit matrix completion
The problem of estimating a matrix based on a set of its observed entries is commonly referred to as the matrix completion problem. In this work, we specifically address the scenario of binary observations, often termed …
Matrix CompletionEnergy-modified Leverage Sampling for Radio Map Construction via Matrix Completion
This paper explores an energy-modified leverage sampling strategy for matrix completion in radio map construction. The main goal is to address potential identifiability issues in matrix completion with sparse observation…
Matrix CompletionStatistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information
This paper investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available. The model consists of two parts. One part is a low-rank matrix induced by unob…
Matrix Completion