Imputation

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10 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Empirical Bayes 1-bit matrix completion
May 10, 2026Takeru MatsudaTensor Completion Empirical Bayes
Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo · Statistical Mathematics Unit, RIKEN Center for Brain Science
Order-Agnostic Autoregressive Modelling with Missing Data
May 7, 2026Ignacio Peis, Pablo M. Olmos, Jes FrellsenMissing Data Imputation
Technical University of Denmark & Pioneer Centre for AI · Universidad Carlos III de Madrid
Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing
May 7, 2026Peilin Zhan, Wei Chen, Weilin Chen +2Learnability Selection Bias
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputation
May 6, 2026Olivia Jullian Parra, Sara Zoccheddu, David Catalan Cerezo +7Heterogeneous Treatment Effects Causal Inferences
Department of Mathematical Modeling and Machine Learning University of Zürich Winterthurerstrasse 190, Zürich 8057 · Department of Mathematics ETH Zürich Rämistrasse 101, Zürich 8092 · Epidemiology, Biostatistics & Prevention Institute (EBPI), University of Zürich Hirschengraben 84, Zürich 8001 +2
PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
May 6, 2026Thomas S. Robinson, Ranjit LallConditional Independence Imputation
Department of Methodology London School of Economics and Political Science London, UK · Department of Politics and International Relations University of OxfordMay Oxford, UK
Predicting missing values: A good idea?
May 5, 2026Stef van BuurenImputation Missing Data
TNO Netherlands Organization for Applied Scientific Research, Leiden · Dept. of Methodology & Statistics, University of Utrecht
HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation
May 4, 2026Fengming Zhang, Wenjie Du, Huan Zhang +2Imputation
Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China · School of Management, Beijing Institute of Technology, Beijing 100081, China · Beijing Lab for System Engineering of Carbon Neutrality, Beijing 100081, China +1
Submodular Benchmark Selection
May 4, 2026Alexander SmolaSubmodular Functions Mutual Information
Boson AI 4677 Old Ironsides Dr Santa Clara CA 95054
Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
May 3, 2026Qiao LiuImputation Missing Data
Department of Biostatistics Yale University
LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations
May 1, 2026Huangbiao Xu, Huanqi Wu, Xiao Ke +1Multimodal Learning Multimodal Reasoning
Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China · Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350108, China · Wangxuan Institute of Computer Technology, Peking University, Beijing 100871, China
SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting
Apr 30, 2026Arnaud ZinflouImputation Accurate Short-Term Electricity Load Forecasting
Hydro-Québec Research Institute Canada
GraphPL: Leveraging GNN for Efficient and Robust Modalities Imputation in Patchwork Learning
Apr 28, 2026Xingjian Hu, Zuoyu Yan, Jianhua Zhu +3Multimodal Learning Federated Graph Learning
Wangxuan Institute of Computer Technology, Peking University, Beijing · Weill Cornell Medicine, Cornell University, New York · Biomedical Informatics, Stony Brook University, New York
Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning
Apr 25, 2026Wugeng Zheng, Ziwen Kan, Katie Wang +2Missing Modalities Imputation
University of Central Florida · Independent Researcher
Theoretical Analysis of Measure Consistency Regularization for Partially Observed Data
Feb 1, 2026Yinsong Wang, Shahin ShahrampourRegularization Imputation
University of Nevada Reno, Reno, NV 89557 USA · Department of Mechanical and Industrial Engineering at Northeastern University, Boston, MA 02115 USA
FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
Dec 17, 2025Runze Li, Hanchen Wang, Wenjie Zhang +4Imputation Fourier
University of New South Wales, Australia · University of Technology Sydney, Australia · The Chinese University of Hong Kong, Shenzhen, China
HAGI++: Head-Assisted Gaze Imputation and Generation
Nov 4, 2025Chuhan Jiao, Zhiming Hu, Andreas BullingImputation
University of Stuttgart, Germany · The Hong Kong University of Science and Technology (Guangzhou), China
Synthetic data for ratemaking: imputation-based methods vs adversarial networks and autoencoders
Sep 2, 2025Yevhen Havrylenko, Meelis Käärik, Artur TuttarSynthetic Data Actuarial Ratemaking
Department of Actuarial Science, Faculty of Business and Economics, University of Lausanne, Switzerland · Institute of Mathematics and Statistics, Faculty of Science and Technology, University of Tartu, Estonia
TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
Aug 24, 2025YongKyung Oh, Dong-Young Lim, Sungil Kim +1Time-Series Classification Missing Data
University of California, Los Angeles, Los Angeles, CA, USA · Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion
Jun 27, 2025Ying Zhang, Yu Zhao, Xuhui Sui +5Federated Graph Learning Knowledge Graph Completion
College of Computer Science, VCIP, DISSec, Nankai University, Tianjin, China · School of Software, Tiangong University, Tianjin, China · Shenzhen Campus of Sun Yat-sen University, Shenzhen, China +1
Temporal Variational Implicit Neural Representations
Jun 2, 2025Batuhan Koyuncu, Rachael DeVries, Ole Winther +1Multivariate Time-Series Imputation
Department of Computer Science, Saarland University · Department of Biology, University of Copenhagen · Novo Nordisk A/S +1
Deep learning with missing data
Apr 21, 2025Tianyi Ma, Tengyao Wang, Richard J. SamworthImputation Exponential Family
Statistical Laboratory, University of Cambridge · Department of Statistics, London School of Economics
Interpretable factorization of clinical questionnaires to identify latent factors of psychopathology
Dec 12, 2023Ka Chun Lam, Francisco Pereira, Bridget W Mahony +1Exploratory Factor Analysis Psychometric Properties
Machine Learning Core National Institute of Mental Health, National Institutes of Health · Section on Developmental Neurogenomics, Human Genetics Branch National Institute of Mental Health, National Institutes of Health
SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
Date pendingXinrui He, Mengting Ai, Junting Wang +2Imputation Mimic-Iv Datasets
University of Illinois Urbana-Champaign Champaign, IL, USA · Mayo Clinic Scottsdale, AZ, USA

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Empirical Bayes 1-bit matrix completion

Takeru Matsuda

Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo · Statistical Mathematics Unit, RIKEN Center for Brain Science

Tensor Completion Empirical Bayes Imputation Bits Item Response Theory

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The problem of predicting unobserved entries in a binary matrix, known as 1-bit matrix completion, has found diverse applications in fields such as recommendation systems. In this study, we develop an empirical Bayes method for 1-bit matrix completion motivated by the Efron--Morris estimator, a matrix generalization of the James--Stein estimator that shrinks singular values toward zero. The proposed method exploits the underlying low-rank structure of binary matrices, drawing parallels with multidimensional item response theory. Simulation studies and real-data applications demonstrate that the proposed method achieves a superior balance of predictive accuracy, calibration reliability (uncertainty quantification), and computational efficiency compared to existing methods.

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