Contact
Ludwig-Maximilians-Universität München
Fakultät für Philosophie, Wissenschaftstheorie
und Religionswissenschaft
Munich Center for Mathematical Philosophy (MCMP)
Geschwister-Scholl-Platz 1
D-80539 München
Office:
Ludwigstr. 31
Room 126
D-80539 München
Email:
Tom.Sterkenburg@lrz.uni-muenchen.de
Website:
http://tomster.userweb.mwn.de/
Further Information
I am currently principal investigator of the German Science Foundation-funded research project “The Epistemology of Statistical Learning Theory.” I hold a BSc in Artificial Intelligence (Amsterdam), a MSc in Logic (Amsterdam), a MSc in History and Philosophy of Science (Utrecht), and a PhD in Philosophy (joint at the University of Groningen and the CWI, the Dutch national research center for mathematics and computer science). In my PhD thesis (2018) I investigated the computability-theoretic approach to probabilistic “universal prediction” as a link between Carnap's inductive logic and modern approaches in machine learning.
Research Interests
My research is in the philosophy of induction and the epistemological foundations of machine learning.
Selected Publications
- On explaining the success of induction, The British Journal for the Philosophy of Science, forthcoming.
- On the truth-convergence of open-minded Bayesianism, The Review of Symbolic Logic, 2022. With Rianne de Heide.
- The no-free-lunch theorems of supervised learning, Synthese, 2021. With Peter Grünwald.
- The meta-inductive justification of induction, Episteme, 2020.
- The meta-inductive justification of induction: The pool of strategies, Philosophy of Science, 2019.
- Putnam’s diagonal argument and the impossibility of a universal learning machine, Erkenntnis, 2019.
- A generalized characterization of algorithmic probability, Theory of Computing Systems, 2017.
- Solomonoff prediction and Occam’s razor, Philosophy of Science, 2016.
Awards
Wolfgang Stegmüller Award for my PhD thesis