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Research Group Tom Sterkenburg


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Tom Sterkenburg

Dr.

Associated JRG Leader Epistemology in ML

Munich Center for Mathematical Philosophy

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

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Timo Freiesleben

Dr.

Munich Center for Mathematical Philosophy

PhD Students

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Katia Parshina

Munich Center for Mathematical Philosophy

Publications @MCML

2021


[1]
G. König, T. Freiesleben, B. Bischl, G. Casalicchio and M. Grosse-Wentrup.
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT).
Preprint (Jun. 2021). arXiv
Abstract

Global model-agnostic feature importance measures either quantify whether features are directly used for a model’s predictions (direct importance) or whether they contain prediction-relevant information (associative importance). Direct importance provides causal insight into the model’s mechanism, yet it fails to expose the leakage of information from associated but not directly used variables. In contrast, associative importance exposes information leakage but does not provide causal insight into the model’s mechanism. We introduce DEDACT - a framework to decompose well-established direct and associative importance measures into their respective associative and direct components. DEDACT provides insight into both the sources of prediction-relevant information in the data and the direct and indirect feature pathways by which the information enters the model. We demonstrate the method’s usefulness on simulated examples.

MCML Authors
Link to website

Timo Freiesleben

Dr.

Munich Center for Mathematical Philosophy

Link to Profile Bernd Bischl

Bernd Bischl

Prof. Dr.

Statistical Learning and Data Science

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Giuseppe Casalicchio

Dr.

Statistical Learning and Data Science

Moritz Grosse-Wentrup

Moritz Grosse-Wentrup

Prof. Dr.

* Former Principal Investigator