Separating the wheat from the chaff: identifying relevant and similar performance data with visual analytics. - Laura von Rüden; Marc-André Hermanns; Michael Behrisch 001; Daniel A. Keim; Bernd Mohr; Felix Wolf 001 (VPA@SC, 2015)

Magnostics: Image-Based Search of Interesting Matrix Views for Guided Network Exploration. - Michael Behrisch 001; Benjamin Bach; Michael Hund; Michael Delz; Laura von Rüden; Jean-Daniel Fekete; Tobias Schreck (IEEE Trans. Vis. Comput. Graph., 2017)

Informed Machine Learning - Towards a Taxonomy of Explicit Integration of Knowledge into Machine Learning. - Laura von Rüden; Sebastian Mayer; Jochen Garcke; Christian Bauckhage; Jannis Schücker (CoRR, 2019)

Street-Map Based Validation of Semantic Segmentation in Autonomous Driving. - Laura von Rüden; Tim Wirtz; Fabian Hüger; Jan David Schneider; Nico Piatkowski; Christian Bauckhage (ICPR, 2020)

Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions. - Laura von Rüden; Sebastian Mayer; Rafet Sifa; Christian Bauckhage; Jochen Garcke (IDA, 2020)

Towards Map-Based Validation of Semantic Segmentation Masks. - Laura von Rüden; Tim Wirtz; Fabian Hüger; Jan David Schneider; Christian Bauckhage (CoRR, 2020)

Towards Intelligent Food Waste Prevention: An Approach Using Scalable and Flexible Harvest Schedule Optimization With Evolutionary Algorithms. - Maurice Günder; Nico Piatkowski; Laura von Rüden; Rafet Sifa; Christian Bauckhage (IEEE Access, 2021)

Explainable Machine Learning with Prior Knowledge: An Overview. - Katharina Beckh; Sebastian Müller; Matthias Jakobs; Vanessa Toborek; Hanxiao Tan; Raphael Fischer 001; Pascal Welke; Sebastian Houben; Laura von Rüden (CoRR, 2021)

Evolutionary Hierarchical Harvest Schedule Optimization for Food Waste Prevention. - Maurice Günder; Nico Piatkowski; Laura von Rüden; Rafet Sifa; Christian Bauckhage (CoRR, 2021)

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey. - Julian Wörmann; Daniel Bogdoll; Etienne Bührle; Han Chen; Evaristus Fuh Chuo; Kostadin Cvejoski; Ludger van Elst; Tobias Gleißner; Philip Gottschall; Stefan Griesche; Christian Hellert; Christian Hesels; et al. (CoRR, 2022)

Informed Pre-Training on Prior Knowledge. - Laura von Rüden; Sebastian Houben; Kostadin Cvejoski; Christian Bauckhage; Nico Piatkowski (CoRR, 2022)

How Does Knowledge Injection Help in Informed Machine Learning? - Laura von Rüden; Jochen Garcke; Christian Bauckhage (IJCNN, 2023)

Harnessing Prior Knowledge for Explainable Machine Learning: An Overview. - Katharina Beckh; Sebastian Müller; Matthias Jakobs; Vanessa Toborek; Hanxiao Tan; Raphael Fischer 001; Pascal Welke; Sebastian Houben; Laura von Rüden (SaTML, 2023)

Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems. - Laura von Rüden; Sebastian Mayer; Katharina Beckh; Bogdan Georgiev; Sven Giesselbach; Raoul Heese; Birgit Kirsch; Julius Pfrommer; Annika Pick; Rajkumar Ramamurthy; Michal Walczak; Jochen Garcke; et al. (IEEE Trans. Knowl. Data Eng., 2023)

Informed Machine Learning: Integrating Prior Knowledge into Data-Driven Learning Systems. - Laura von Rüden (2023)