Machine learning methods over relational databases, composed of tables with interlinked tuples.
DBs as hetero-graphs. ML on structured data.
Tabular ML via decision trees.
Feature generation on databases, including tabular and graph features.
Low-dimensional embedding of structures.
Crash course on deep learning.
Message-passing graph neural networks (MPGNNs).
Foundation models on tabular and relational data.