CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation
Aditya Gorla, Ryan Wang, Zhengtong Liu, Ulzee An, Sriram Sankararaman
Abstract
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 CACTIto 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.
BibTeX
@inproceedings{
gorla2025cacti,
title={{CACTI}: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation},
author={Aditya Gorla and Ryan Wang and Zhengtong Liu and Ulzee An and Sriram Sankararaman},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=IYLNdCII48}
}