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 four work groups:

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

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Four papers accepted at SDM 2023

Four papers from our group have been accepted at SDM 2023.

Algorithmus, wieso tust du das? []

Sebastian Tschiatschek auf über Konsequenzen des Einsatzes von Algorithmen in demokratischen Prozessen.

Paper accepted at LoG 2022

The paper "Gradual Weisfeiler-Leman: Slow and Steady Wins the Race" has been accepted at LoG

Paper accepted at IEEE TAI

The paper "Replication Robust Payoff Allocation in Submodular Cooperative Games" has been accepted for publication in the IEEE Transactions on Artificial Intelligence.

Paper accepted at NeurIPS 2022

The paper "Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited" has been accepted at the NeurIPS