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.

Team of research group DM

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 News

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Two papers accepted at The Web Conference 2023

Two papers from our group have been accepted at The Web Conference 2023 (formerly WWW)

Paper accepted at AAMAS

The paper "Learning Constraints From Human Stop-feedback in Reinforcement Learning" has been accepted at AAMAS.

Four papers accepted at SDM 2023

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

Algorithmus, wieso tust du das? [derstandard.at]

Sebastian Tschiatschek auf derstandard.at 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

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