Hybrid Ranking and Regression for Algorithm Selection. - Jonas Hanselle; Alexander Tornede; Marcel Wever; Eyke Hüllermeier (KI, 2020)

Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. - Jonas Hanselle; Alexander Tornede; Marcel Wever; Eyke Hüllermeier (PAKDD (1), 2021)

Distributed Data Streams. - Jannik Castenow; Björn Feldkord; Jonas Hanselle; Till Knollmann; Manuel Malatyali; Friedhelm Meyer auf der Heide (Algorithms for Big Data, 2022)

HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. - Lukas Fehring; Jonas Hanselle; Alexander Tornede (CoRR, 2022)

Probabilistic Scoring Lists for Interpretable Machine Learning. - Jonas Hanselle; Johannes Fürnkranz; Eyke Hüllermeier (DS, 2023)

Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. - Jonas Hanselle; Jaroslaw Kornowicz; Stefan Heid; Kirsten Thommes; Eyke Hüllermeier (LWDA, 2023)

Towards Green Automated Machine Learning: Status Quo and Future Directions. - Tanja Tornede; Alexander Tornede; Jonas Hanselle; Felix Mohr; Marcel Wever; Eyke Hüllermeier (J. Artif. Intell. Res., 2023)

PyExperimenter: Easily distribute experiments and track results. - Tanja Tornede; Alexander Tornede; Lukas Fehring; Lukas Gehring; Helena Graf; Jonas Hanselle; Felix Mohr; Marcel Wever (J. Open Source Softw., 2023)

Learning decision catalogues for situated decision making: The case of scoring systems. - Stefan Heid; Jonas Hanselle; Johannes Fürnkranz; Eyke Hüllermeier (Int. J. Approx. Reason., 2024)

Conformal Prediction without Nonconformity Scores. - Jonas Hanselle; Alireza Javanmardi; Tobias Florin Oberkofler; Yusuf Sale; Eyke Hüllermeier (UAI, 2025)

Probabilistic scoring lists for interpretable machine learning. - Jonas Hanselle; Stefan Heid; Johannes Fürnkranz; Eyke Hüllermeier (Mach. Learn., 2025)