Robust Matrix Completion

Latest papers

8 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Large-Scale Evaluation of Advanced Imputation Methods for Missing Values in Smart Meter Data
Aug 21, 2026Daniela Stojcheska, Marija Markovska, Dimitar Taskovski +2Robust Matrix Completion Incomplete Data Imputation
FEEIT Ss. Cyril and Methodius University in Skopje Skopje, North Macedonia · Elektrodistribucija DOOEL EVN Group Skopje, North Macedonia
You Don't Need to Run Every Eval
Jun 22, 2026Yuchen Zeng, Dimitris PapailiopoulosLLM Evaluation Robust Matrix Completion
Microsoft Research, AI Frontiers
Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data
May 25, 2026Yutong Chao, Resat Gökhan, Jalal Etesami +1Latent Variable Models Robust Matrix Completion
School of Computation, Information and Technology, Technical University of Munich, Germany · Munich Institute of Robotics and Machine Intelligence · Department of Economics, Virginia Tech, USA
Acting on the Unseen: Communication-Free Collaborative Filtering for Decentralized Multi-Robot Task Allocation
May 25, 2026Alexander Apartsin, Yigal Meshulam, Yehudit ApersteinRobust Matrix Completion Multi-Robot Task Allocation
Holon Institute of Technology, Holon, Israel. · Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
Sample-efficient inductive matrix completion with noise and inexact side-information
May 16, 2026Yuepeng Yang, Cong MaRobust Matrix Completion Matrix Optimization
Department of Statistics and Data Sciences, Yale University · Department of Statistics, University of Chicago
Valid Best-Model Identification for LLM Evaluation via Low-Rank Factorization
May 11, 2026Elad Tolochinsky, Yaniv Tenzer, Yaniv RomanoModel Selection Multi-Armed Bandits
Department of Computer Science, Technion – Israel Institute of Technology · Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology
BVSIMC: Bayesian Variable Selection-Guided Inductive Matrix Completion for Improved and Interpretable Drug Discovery
Mar 19, 2026Sijian Fan, Liyan Xiong, Dayuan Wang +2Robust Matrix Completion Drug Discovery
Department of Statistics, George Mason University
Towards Reliable Recommender Systems for Rating Data
Dec 30, 2024Aurore Archimbaud, Andreas Alfons, Ines WilmsRobust Matrix Completion Adversarial Attacks
TBS Business School · Erasmus University Rotterdam · Maastricht University

Aug 21, 2026J/K move · Enter open · S save

Large-Scale Evaluation of Advanced Imputation Methods for Missing Values in Smart Meter Data

Daniela Stojcheska, Marija Markovska, Dimitar Taskovski, Branislav Gerazov+1

FEEIT Ss. Cyril and Methodius University in Skopje Skopje, North Macedonia · Elektrodistribucija DOOEL EVN Group Skopje, North Macedonia

Robust Matrix Completion Incomplete Data Imputation Time Series Imputation Autoencoders

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Accurate and reliable collection of electricity consumption data through Advanced Metering Infrastructure (AMI) is of great importance for the operation of smart grids, especially for the detection of non-technical losses (NTL). However, real-world datasets frequently suffer from missing values due to communication failures. This paper presents an empirical evaluation of three advanced algorithms for large-scale data imputation: the Optimally Weighted Average (OWA) method, Low-Rank Matrix Completion via SoftImpute, and a Shape-Modeling Autoencoder. Existing studies on missing value imputation in electricity consumption data often lack validation on larger datasets. Therefore, the goal of this paper is to validate the selected algorithms on a large-scale real-world electricity consumption dataset from North Macedonia that includes 17,428 commercial smart meters over two years. The robustness of each algorithm is evaluated by simulating continuous gaps in the data ranging from 1 to 168 hours. The results indicate that OWA provides the lowest overall reconstruction error across the evaluated gap sizes and strong stability in worst-case scenarios for gaps of up to one week. In contrast, the autoencoder exhibits higher variance, while SoftImpute has stable but inferior accuracy. These findings suggest that imputation methods should be selected based on the characteristics of load curve data and highlight the potential for hybrid algorithmic architectures in future grid management systems.