Christian Scholbeck

About

I am jointly affiliated with the working group Methods for Missing Data, Model Selection and Model Averaging under supervision of Prof. Dr. Heumann and the chair of Statistical Learning and Data Science under supervision of Prof. Dr. Bischl at the Department of Statistics at Ludwig-Maximilians-University Munich. My research focuses on interpretable machine learning.

E-mail

christian.scholbeck [at] stat.uni-muenchen.de

Research interests

You can find me on

References

  1. Scholbeck CA, Funk H, Casalicchio G (2022) Algorithm-Agnostic Interpretations for Clustering.
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  2. Scholbeck CA, Casalicchio G, Molnar C, Bischl B, Heumann C (2022) Marginal Effects for Non-Linear Prediction Functions.
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  3. Molnar C, König G, Herbinger J, Freiesleben T, Dandl S, Scholbeck CA, Casalicchio G, Grosse-Wentrup M, Bischl B (2021) General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.
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  4. Scholbeck CA, Molnar C, Heumann C, Bischl B, Casalicchio G (2020) Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. In: In: Cellier P , In: Driessens K (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, pp. 205–216. Springer International Publishing, Cham.
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