Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality
tabular data for model training remains a significant obstacle. Numerous works have …

Feature transformation is to derive a new feature set from original features to augment the AI
power of data. In many science domains such as material performance screening, while …

Tabular data is one of the most widely used formats across industries, driving critical
applications in areas such as finance, healthcare, and marketing. In the era of data-centric …

Polymer property performance prediction aims to forecast specific features or attributes of
polymers, which has become an efficient ap-proach to measuring their performance …

In addressing the imbalanced issue of data within the realm of Natural Language
Processing, text data augmentation methods have emerged as pivotal solutions. This data …

Federated Learning faces significant challenges in statistical and system heterogeneity,
along with high energy consumption, necessitating efficient client selection strategies …

Feature selection aims to identify the optimal feature subset for enhancing downstream
models. Effective feature selection can remove redundant features, save computational …

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 …

Data augmentation is a series of techniques that generate high-quality artificial data by
manipulating existing data samples. By leveraging data augmentation techniques, AI …

Feature transformation aims to reconstruct the feature space of raw features to enhance the
performance of downstream models. However, the exponential growth in the combinations …