arXiv:2607. 29177v1 Announce Type: cross Abstract: Utility data (e.
arXiv:2606. 05073v1 Announce Type: new Abstract: Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values.
Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many real-world datasets, however, missingness may arise from two distinct sources: some entries are meaningfully missing (intrinsically absent and semantically valid), while others are missing due to the observation process and should be imputed.
arXiv:2609.37632v1 Announce Type: cross Abstract: Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent perform...
arXiv:2609.15284v1 Announce Type: new Abstract: Missing values are ubiquitous in heterogeneous data mining, where numerical, categorical, and binary variables often coexist. Many imputation methods,...
arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.
RDDMPI introduces a residual denoising diffusion model for multivariate time series imputation. By decomposing the missing signal into a baseline reconstruction and a residual uncertainty component, the method conditions the diffusion process on both the completed signal and its latent representation, using a reliability-aware mechanism to balance baseline influence. Experiments on benchmark datasets show that this approach improves reconstruction accuracy and uncertainty quantification compared to prior diffusion-based methods.
arXiv:2603. 25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.
The paper introduces a pre‑training pipeline that creates transformer‑based imputation specialists for tabular data with specific missingness patterns. By featurizing entries, generating synthetic data with configurable missingness modules, and fitting on millions of synthetic tables, the pipeline produces pattern‑specific models that outperform dedicated methods for each missingness pattern. A default model trained only on MCAR data, TabImpute, remains robust across all tested patterns, and the authors release the pipeline, models, and a new benchmark of 42 datasets and 11 missingness patterns.
The paper introduces the Masked Diffusion Time-series Imputation Model (MDTIM), which uses a masked diffusion training paradigm to directly predict original values for time series imputation. It separates missing and observed data via a MASK token and employs Stochastic Discretization to convert continuous values into ordinal-aware tokens, preserving temporal dynamics. Experiments on multiple benchmarks show that MDTIM outperforms existing deterministic and generative baselines in robustness and scalability across various missing data scenarios.
arXiv:2606. 17106v1 Announce Type: new Abstract: Laboratory tests in electronic health records are collected irregularly, and the absence of a test order can be as informative as the measurement itself.
LoaDiff is a diffusion-based generative model that produces year-long, sub-hourly smart‑meter electricity consumption time series. It can be conditioned on static household attributes like appliance ownership and dynamic factors such as calendar dates and outdoor temperature. Evaluations on three residential datasets show that LoaDiff generates realistic, diverse load profiles, limits memorization, retains useful information for downstream tasks, and responds coherently to conditioning changes.
Missing data is a persistent obstacle in scientific, social science, and public health research, often biasing analyses and placing accountability on analysts for how they handle missing values. We introduce ImputeViz, an integrated visual analytics dashboard that supports diagnosing missingness, configuring imputation models, and evaluating results.