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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.02306 |
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| _version_ | 1866913872078700544 |
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| author | Gorla, Aditya Wang, Ryan Liu, Zhengtong An, Ulzee Sankararaman, Sriram |
| author_facet | Gorla, Aditya Wang, Ryan Liu, Zhengtong An, Ulzee Sankararaman, Sriram |
| contents | We present CACTI, a masked autoencoding approach for imputing tabular data that leverages the structure in missingness patterns and contextual information. Our approach employs a novel median truncated copy masking training strategy that encourages the model to learn from empirical patterns of missingness while incorporating semantic relationships between features - captured by column names and text descriptions - to better represent feature dependence. These dual sources of inductive bias enable CACTI to outperform state-of-the-art methods - an average $R^2$ gain of 7.8% over the next best method (13.4%, 6.1%, and 5.3% under missing not at random, at random and completely at random, respectively) - across a diverse range of datasets and missingness conditions. Our results highlight the value of leveraging dataset-specific contextual information and missingness patterns to enhance imputation performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02306 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation Gorla, Aditya Wang, Ryan Liu, Zhengtong An, Ulzee Sankararaman, Sriram Machine Learning We present CACTI, a masked autoencoding approach for imputing tabular data that leverages the structure in missingness patterns and contextual information. Our approach employs a novel median truncated copy masking training strategy that encourages the model to learn from empirical patterns of missingness while incorporating semantic relationships between features - captured by column names and text descriptions - to better represent feature dependence. These dual sources of inductive bias enable CACTI to outperform state-of-the-art methods - an average $R^2$ gain of 7.8% over the next best method (13.4%, 6.1%, and 5.3% under missing not at random, at random and completely at random, respectively) - across a diverse range of datasets and missingness conditions. Our results highlight the value of leveraging dataset-specific contextual information and missingness patterns to enhance imputation performance. |
| title | CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.02306 |