Tabular Data-centric AI: Challenges, Techniques and Future Perspectives

Tabular data are the most widely used data formats in almost every application domain, such as, biology, ecology, and material science. The purpose of tabular data-centric AI is to use AI to augment the predictive power of tabular data to get better AI. Tabular data-centric AI is essential because it can reconstruct distance measures, reshape discriminative patterns, and improve data AI readiness (structural, predictive, interaction, and expression levels), which is significant in industries and real-world deployments. Therefore, our tutorial is designed to capture the interest of professionals with expertise in artificial intelligence, machine learning, and data mining, as well as researchers engaged in specific application areas and interdisciplinary studies. Examples of such applications include quality control, predictive maintenance, supply chain optimization, process efficiency improvements, biomarker identification, material performance screening. In this tutorial, we will explore the emerging field of Tabular Data-Centric AI. Our discussion will provide a comprehensive overview of this domain: (1) We will demonstrate the different settings within this research domain based on distinct application scenarios. (2) We will identify and explain the significant challenges encountered in tabular data-centric AI. (3) We will highlight existing methods and benchmarks. (4) We will discuss future potential directions for this domain and examine its interconnections with other research areas. To enhance the learning experience, this tutorial will include a hands-on section designed to teach participants the fundamental aspects of developing, evaluating and visualizing techniques in tabular data-centric AI. After this tutorial, attendees will have a deep understanding of tabular data-centric AI research, including its key challenges, seminal techniques, and insights into integrating tabular data-centric AI into their own research.