AutoML for Predictive Maintenance: One Tool to RUL Them All
T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020.

Towards Meta-Algorithm Selection
A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification
M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.

Towards Automated Machine Learning for Multi-Label Classification
M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.

Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking
A. Tornede, M.D. Wever, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146.

From Automated to On-The-Fly Machine Learning
F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., Bonn, 2019, pp. 273–274.

Automating Multi-Label Classification Extending ML-Plan
M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.

jPL: A Java-based Software Framework for Preference Learning
P. Gupta, A. Hetzer, T. Tornede, S. Gottschalk, A. Kornelsen, S. Osterbrink, K. Pfannschmidt, E. Hüllermeier, in: 2017.

Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction
A. Hetzer, T. Tornede, Solving the Container Pre-Marshalling Problem Using Reinforcement Learning and Structured Output Prediction, Universität Paderborn, 2017.