Tabular Data Imputation

Tabular data imputation aims to fill in missing values in datasets, a crucial preprocessing step for many data analysis and machine learning tasks. Recent research heavily focuses on leveraging advanced generative models, such as diffusion models and transformer-based architectures (including adaptations of BERT), to capture complex data relationships and generate realistic imputations. These methods are being evaluated and improved based on criteria like imputation accuracy, computational efficiency, and the preservation of data structure and statistical properties. The resulting improvements in data quality have significant implications for various fields, enabling more robust analyses and more accurate predictions from incomplete datasets.