📝 SummarXiv

Abstract

Missing data is a pervasive problem in tabular settings. Existing solutions range from simple averaging to complex generative adversarial networks. However, due to huge variance in performance across real-world domains and time-consuming hyperparameter tuning, no default imputation method exists. 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, which enables a $100\times$ speedup over the previous TabPFN imputation method, (ii) a synthetic training data generation pipeline incorporating realistic missingness patterns, which boosts test-time performance, and (iii) MissBench, a comprehensive benchmark for evaluation of imputation methods with $42$ OpenML datasets and $13$ missingness patterns. MissBench spans domains such as medicine, finance, and engineering, showcasing TabImpute's robust performance compared to $11$ established imputation methods.