We present RelBench, a public benchmark for solving predictive tasks in relational
databases with deep learning. RelBench provides databases and tasks spanning diverse …

Much of the world's most valued data is stored in relational databases and data warehouses,
where the data is organized into tables connected by primary-foreign key relations …

Automated Machine Learning (AutoML) offers a promising approach to streamline the
training of machine learning models. However, existing AutoML frameworks are often limited …

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

Recommendation systems predominantly utilize two-tower architectures, which evaluate
user-item rankings through the inner product of their respective embeddings. However, one …

This paper explores the integration of Business Analytics (BA) with Artificial Intelligence (AI)
by considering evidence from existing literature through an augmented research process …

Predictive tasks on relational databases are critical in real-world applications spanning e-
commerce, healthcare, and social media. To address these tasks effectively, Relational …

Transformer models have continuously expanded into all machine learning domains
convertible to the underlying sequence-to-sequence representation, including tabular data …

The recent integration of Artificial Intelligence (AI) into smart consumer electronics and
sustainable healthcare has shown promising outcomes. However, challenges persist in …

Much of the world's most valued data is stored in relational databases and data warehouses,
where the data is organized into many tables connected by primary-foreign key relations …