Hybrid Methods for Robust Tabular Data Imputation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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 SCR-MF, a two‑stage workflow for single‑cell RNA sequencing imputation that first detects dropout events with scRecover and then imputes missing values using the non‑parametric missForest algorithm. Benchmarking on public and simulated datasets shows that SCR‑MF delivers robust, interpretable results that match or surpass existing methods while maintaining biological fidelity. Runtime analysis indicates that SCR‑MF balances accuracy with computational efficiency, making it well suited for mid‑scale single‑cell studies.
arXiv:2607. 07767v1 Announce Type: cross Abstract: Missing values undermine statistical inference and machine learning pipelines, yet most imputation methods rely on heuristics or restrictive parametric assumptions that ignore the joint data distribution.
arXiv:2601. 14653v3 Announce Type: replace Abstract: Missing data in single-cell sequencing datasets poses significant challenges for extracting meaningful biological insights.
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.
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.