Munich Center for Mathematical Philosophy
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.
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.
Munich Center for Mathematical Philosophy
Statistical Learning and Data Science
Statistical Learning and Data Science
Moritz Grosse-Wentrup
Prof. Dr.
* Former Principal Investigator
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2025-03-17 - Last modified: 2025-03-17