TabImpute: Universal Zero-Shot Imputation for Tabular Data
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arXiv
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| Format: | Preprint |
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2025
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| author | Feitelberg, Jacob Saha, Dwaipayan Choi, Kyuseong Ahmad, Zaid Agarwal, Anish Dwivedi, Raaz |
| author_facet | Feitelberg, Jacob Saha, Dwaipayan Choi, Kyuseong Ahmad, Zaid Agarwal, Anish Dwivedi, Raaz |
| contents | Missing data is a widespread problem in tabular settings. Existing solutions range from simple averaging to complex generative adversarial networks, but due to each method's large variance in performance across real-world domains and time-consuming hyperparameter tuning, no universal imputation method exists. This performance variance is particularly pronounced in small datasets, where the models have the least amount of information. Building on TabPFN, a recent tabular foundation model for supervised learning, we propose TabImpute, a pre-trained transformer that delivers accurate and fast zero-shot imputations, requiring no fitting or hyperparameter tuning at inference time. To train and evaluate TabImpute, we introduce (i) an entry-wise featurization for tabular settings, enabling a 100x speedup over the previous TabPFN imputation method, (ii) a synthetic training data generation pipeline incorporating a diverse set of missingness patterns to enhance accuracy on real-world missing data problems, and (iii) MissBench, a comprehensive benchmark with 42 OpenML tables and 13 new missingness patterns. MissBench spans domains such as medicine, finance, and engineering, showcasing TabImpute's robust performance compared to numerous established imputation methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02625 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TabImpute: Universal Zero-Shot Imputation for Tabular Data Feitelberg, Jacob Saha, Dwaipayan Choi, Kyuseong Ahmad, Zaid Agarwal, Anish Dwivedi, Raaz Machine Learning Missing data is a widespread problem in tabular settings. Existing solutions range from simple averaging to complex generative adversarial networks, but due to each method's large variance in performance across real-world domains and time-consuming hyperparameter tuning, no universal imputation method exists. This performance variance is particularly pronounced in small datasets, where the models have the least amount of information. Building on TabPFN, a recent tabular foundation model for supervised learning, we propose TabImpute, a pre-trained transformer that delivers accurate and fast zero-shot imputations, requiring no fitting or hyperparameter tuning at inference time. To train and evaluate TabImpute, we introduce (i) an entry-wise featurization for tabular settings, enabling a 100x speedup over the previous TabPFN imputation method, (ii) a synthetic training data generation pipeline incorporating a diverse set of missingness patterns to enhance accuracy on real-world missing data problems, and (iii) MissBench, a comprehensive benchmark with 42 OpenML tables and 13 new missingness patterns. MissBench spans domains such as medicine, finance, and engineering, showcasing TabImpute's robust performance compared to numerous established imputation methods. |
| title | TabImpute: Universal Zero-Shot Imputation for Tabular Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.02625 |