Abstract Language models pretrained on large collections of tabular data have
demonstrated their effectiveness in several downstream tasks. However, many of these …

Deep learning (DL) models for tabular data problems are receiving increasingly more
attention, while the algorithms based on gradient-boosted decision trees (GBDT) remain a …

Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream
task execution by retrieving additional information, such as knowledge, skills, and tools from …

Tabular data remains one of the most prevalent data types across a wide range of real-world
applications, yet effective representation learning for this domain poses unique challenges …

All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data
which is available in so called tabular form, where each record is composed of a number of …

In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks
(GNNs), a domain where deep learning-based approaches have increasingly shown …

Recommender systems play important roles in various applications such as e-commerce,
social media, etc. Conventional recommendation methods usually model the collaborative …

Click-through rate (CTR) prediction is crucial for personalized online services. Sample-level
retrieval-based models, such as RIM, have demonstrated remarkable performance …

Tabular data, a prevalent data type across various domains, presents unique challenges
due to its heterogeneous nature and complex structural relationships. Achieving high …

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