Portfolio item number 1
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portfolio
Portfolio item number 2
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publications
Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems
Published in IEEE TKDE, 2021
Integration prior knowledge into the machine learning pipeline
Recommended citation: L. Von Rueden, S. Mayer, K. Beckh, B. Georgiev, S. Giesselbach, R. Heese, B. Kirsch, J. Pfrommer, A. Pick, R. Ramamurthy, M. Walczak, J. Garcke, C. Bauckhage, and J. Schuecker, “Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 1, pp. 614-633 https://ieeexplore.ieee.org/abstract/document/9429985
A Quantitative Human-Grounded Evaluation Process for Explainable Machine Learning
Published in LWDA, 2022
Evaluating user independent explanation properties before user dependent properties
Recommended citation: K. Beckh, S. Müller, S. Rüping. "A Quantitative Human-Grounded Evaluation Process for Explainable Machine Learning." LWDA. 2022. https://ceur-ws.org/Vol-3341/KDML-LWDA_2022_CRC_551.pdf
Harnessing Prior Knowledge for Explainable Machine Learning: An Overview
Published in IEEE SaTML, 2023
How prior knowledge can be utilized for explainable machine learning
Recommended citation: K. Beckh, S. Müller, M. Jakobs, V. Toborek, H. Tan, R. Fischer, P. Welke, S. Houben, L. von Rueden. "Harnessing Prior Knowledge for Explainable Machine Learning: An Overview," 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Raleigh, NC, USA, 2023, pp. 450-463 https://www.researchgate.net/publication/371243326_Harnessing_Prior_Knowledge_for_Explainable_Machine_Learning_An_Overview
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning
Published in ECML PKDD, 2023
The role of the Rashomon effect in explainable machine learning
Recommended citation: Müller, S., Toborek, V., Beckh, K., Jakobs, M., Bauckhage, C., Welke, P. (2023). An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science, vol 14171. Springer, Cham. https://arxiv.org/abs/2306.15786
talks
Poster presentation on empirical evaluation of the Rashomon effect in explainable machine learning
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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