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Research Group Tom Sterkenburg


Link to website at LMU

Tom Sterkenburg

Dr.

Associated JRG Leader Epistemology in ML

Tom Sterkenburg

leads the Emmy Noether Junior Research Group ‘From Bias to Knowledge: The Epistemology of Machine Learning’ at LMU Munich.

His group’s research is in the epistemological foundations of machine learning. The group uses the mathematical theory of machine learning to study epistemological questions around machine learning and its reliability, with a particular focus on the notion of inductive bias. The group also works on other topics where machine learning and the philosophy of science meet, including explanation and representation. Supported by DFG funding, the group investigates novel research directions that both complement and extend MCML’s scope while strengthening ties to the center.

Team members @MCML

PostDocs

Link to website

Timo Freiesleben

Dr.

PhD Students

Link to website

Katia Parshina

Recent News @MCML

Link to Tom Sterkenburg Wins Karl-Heinz Hoffmann Prize of the Bavarian Academy of Sciences

08.12.2025

Tom Sterkenburg Wins Karl-Heinz Hoffmann Prize of the Bavarian Academy of Sciences

Honored for His Work at the Intersection of Philosophy, Statistics, and Machine Learning

Link to MCML at NeurIPS 2025

28.11.2025

MCML at NeurIPS 2025

47 Accepted Papers (38 Main, and 9 Workshops)

Publications @MCML

2025


[2] A* Conference
G. König • H. Fokkema • T. Freiesleben • C. Mendler-Dünner • U. von Luxburg
Performative Validity of Recourse Explanations.
NeurIPS 2025 - 39th Conference on Neural Information Processing Systems. San Diego, CA, USA, Nov 30-Dec 07, 2025. To be published. Preprint available. URL

2021


[1]
G. KönigT. FreieslebenB. BischlG. CasalicchioM. Grosse-Wentrup
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT).
Preprint (Jun. 2021). arXiv