The research group Data Mining and Machine Learning investigates novel approaches to exploratory data analysis, unsupervised, semi-supervised and supervised learning. We focus on methods for various data types including texts, graphs, high-dimensional feature vectors and other complex structures. We consider different tasks, e.g., representation learning, embedding, clustering, causality detection, classification and reinforcement learning.

The research group Data Mining and Machine Learning consists of six work groups:

Our methods are inspired by challenges arising from different application areas, e.g. medicine, neuroscience, pharmacoinformatics, renewable energies and social sciences.

Team of research group DM

Publications of research group DM

Projects of research group DM

 News

Paper accepted at JMLR

Our paper "Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length" has been accepted at the Journal of Machine Learning Research.

MLG@ECML PKDD 2024

We are excited to announce that the 22nd International Workshop on Mining and Learning with Graphs (MLG) will again be held jointly with the ECML PKDD this September (either...

Franka presented the paper "Removing Redundancy in Graph Neural Networks" by Franka Bause, Samir Moustafa, Wilfried Gansterer, Nils M Kriege (Link:...

Mit klugen Algorithmen die Qualität von ChatGPT messen [Rudolphina]

Benjamin Roth untersucht, wie Algorithmen uns bei der Verarbeitung von großen Textmengen unterstützen können. Gemeinsam mit seinem Team entwickelt er Programme, mit denen man...

We are excited to announce that our paper "Specifying Prior Beliefs over DAGs in Deep Bayesian Causal Structure Learning" has been accepted at the European Conference on...