One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data

[2510.02625] One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data Skip to main content Search Submit Donate Log in Computer Science > Machine Learning arXiv:2510.02625 (cs) [Submitted on 3 Oct 2025 ( v1 ), last revised 28 Aug 2026 (this version, v6)] Title: One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data Authors: Jacob Feitelberg , Dwaipayan Saha , Kyuseong Choi , Zaid Ahmad , Anish Agarwal , Raaz Dwivedi View PDF HTML (experimental) Abstract: Missing data in tabular datasets forces practitioners into a hard choice: deploy a general-purpose imputer that may perform poorly for the problem at hand, or wait for someone to design a specialized algorithm. This problem is worsened by the fact that real-world missingness rarely satisfies the textbook missing completely at random (MCAR) assumption, as entries are often missing not at random (MNAR), where the probability of being observed depends on the missing data itself. We collapse this trade-off into a single pre-training pipeline that builds transformer-based imputation specialists through three components: an entry-wise featurization that recasts imputation as supervised prediction over row--column context, a synthetic data generator with pluggable missingness modules, and prior-data fitting on millions of synthetic tables. Swapping only the missingness module, with no changes to architecture, loss, or training, yields a pattern-specific specialist that outperforms methods purpose-built for that pattern. We validate this on MissBench, a new benchmark of 42 OpenML datasets and 11 missingness patterns (including 9 MNAR variants) spanning medicine, finance, and engineering. Remarkably, training exclusively on MCAR yields a default model---TabImpute---robust across all tested patterns. In addition, the pattern-specific specialists produced by our pipeline outperform the 14 established baselines on their target patterns. We open-source the pipeline, models, and benchmark. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2510.02625 [cs.LG] (or arXiv:2510.02625v6 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2510.02625 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jacob Feitelberg [ view email ] [v1] Fri, 3 Oct 2025 00:08:54 UTC (352 KB) [v2] Fri, 10 Oct 2025 15:55:12 UTC (352 KB) [v3] Sat, 10 Jan 2026 17:10:11 UTC (347 KB) [v4] Tue, 17 Feb 2026 14:59:03 UTC (2,622 KB) [v5] Tue, 18 Aug 2026 00:58:33 UTC (378 KB) [v6] Fri, 28 Aug 2026 02:36:13 UTC (385 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.LG < prev | next > new | recent | 2025-10 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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