Cross Table

Cross-table analysis focuses on leveraging information across multiple, heterogeneous tabular datasets to improve machine learning model performance and efficiency. Current research emphasizes developing pretrained models, often employing Transformer architectures, that learn generalizable representations from diverse tables, addressing challenges like inconsistent schemas and data types through techniques such as federated learning and self-supervised learning (e.g., masked cell recovery). These advancements aim to enhance the accuracy and speed of downstream tasks like cardinality estimation and tabular prediction, impacting various fields by enabling more efficient data analysis and improved model generalizability.