Hugging Face Trending Papers
Hybrid Methods for Robust Tabular Data Imputation
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv Machine Learning
Oct 2
Hybrid Methods for Robust Tabular Data Imputation
The paper introduces two hybrid imputation methods, NuclearForest and SoftForest, that combine a low‑rank initialization (using Singular Value Thresholding or SoftImpute) with a single Random Forest refinement. These methods capture global covariance patterns and local nonlinear dependencies, respectively, and are benchmarked against seven existing techniques across MCAR, MAR, and MNAR scenarios. Results show comparable or superior imputation accuracy to state‑of‑the‑art iterative methods while achieving significant speedups of roughly 5.8× and 9.5× over MissForest.
arXiv Machine Learning
Aug 19
One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data
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.
arXiv AI
Sep 21
A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
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 Machine Learning
1d ago
When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch
arXiv:2610.20342v1 Announce Type: new Abstract: Missing data is a pervasive challenge in machine learning, often degrading predictive performance and complicating model deployment. Most existing appr...
arXiv Machine Learning
Jun 4
Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
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.
Hugging Face Trending Papers
Jun 3
Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
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.