3 papers
cs.LG2026
Interpretable reinforcement learning with decision-tree pruning
Mark Leon Ringer, Michel Tokic
Reinforcement learning policies are difficult to inspect, but interpreting them is a prerequisite for trustworthiness. Converting a trained policy into explicit decision-tree rules…
cs.LG2025
TSFM in-context learning for time-series classification of bearing-health status
Michel Tokic, Slobodan Djukanović, Anja von Beuningen +1
We introduce a classification method based on in-context learning using time-series foundation models (TSFMs). We demonstrate how data not included in the TSFM training can be clas…
cs.LG2020
Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics
Manuel A. Roehrl, Thomas A. Runkler, Veronika Brandtstetter +2
Identifying accurate dynamic models is required for the simulation and control of various technical systems. In many important real-world applications, however, the two main modeli…