Informed Active Learning with Decision Trees to Balance Exploration and Exploitation
M. Schöne, B. Jaster, J. Bültemeier, M. Kohlhase, in: Institute for Data Science Solutions (Ed.), Kongress KI@HS Zukunft Im Fokus – Posterbeiträge, Hochschule Bielefeld, Bielefeld, 2025, pp. 26–27.

AI Workflow for Scarce Data: A Modular Approach to Optimise Processes
J. Bültemeier, C.-A. Holst, V. Lohweg, M. Schöne, B. Jaster, M. Kohlhase, in: 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2025, pp. 1–4.

Incorporation of structural properties of the response surface into oblique model trees
M. Schöne, M. Kohlhase, O. Nelles, At - Automatisierungstechnik 73 (2025) 727–739.

Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit
M. Schöne, B. Jaster, J. Bültemeier, J. Kösters, C.-A. Holst, M. Kohlhase, in: IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), IEEE, 2025, pp. 1–9.

Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit
M. Schöne, B. Jaster, J. Bültemeier, J. Kösters, C.-A. Holst, M. Kohlhase, in: IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), IEEE, 2025, pp. 1–9.

Evaluierung der Leistungsfähigkeit von LSTM-Modellen für die Approximation physikalischer Systeme 
V. Katter, B. Jaster, M. Schöne, Schriftenreihe des Institute for Data Science Solutions 2 (2024).

AI for Scarce Data (AScaDa) — Maschinelles Lernen und Informationsfusion zur nachhaltigen Nutzung von Labor- und Kundendaten
M. Schöne, J. Bültemeier, in: Institute for Data Science Solutions (Ed.), Kongress KI@HS Solutions Im Fokus – Posterbeiträge, Hochschule Bielefeld, Bielefeld, 2024, pp. 28–29.

Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs
J. Bültemeier, M. Schöne, M. Kohlhase, C.-A. Holst, V. Lohweg, O. Nelles, in: H. Schulte, F. Hoffmann, R. Mikut (Eds.), Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024, KIT Scientific Publishing, 2024, pp. 217–231.

Active Learning mit dem GUIDE-Entscheidungsbaum
J. Kösters, M. Schöne, Active Learning mit dem GUIDE-Entscheidungsbaum, n.d.

Benchmarking of Machine Learning Models for Tabular Scarce Data
J. Kösters, M. Schöne, M. Kohlhase, Benchmarking of Machine Learning Models for Tabular Scarce Data, n.d.

Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, M. Kuhn, in: H. Yin, D. Camacho, P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, Springer International Publishing, Cham, 2022, pp. 379–390.

Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD
M. Hanitz, M. Schöne, T. Voigt, M. Kohlhase, in: P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, MLDM 2022, ibai-publishing, Leipzig, 2022, pp. 121–135.

Curvature-Oriented Splitting for Multivariate Model Trees
M. Schöne, M. Kohlhase, in: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2021, pp. 01–09.

Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP
J. Ewerszumrode, M. Schöne, S. Godt, M. Kohlhase, in: H. Schulte, F. Hoffmann, R. Mikut (Eds.), Proceedings - 31. Workshop Computational Intelligence , KIT Scientific Publishing, 2021, pp. 285–305.

Space-Filling Designs for Experiments with Assembled Products
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, M. Kuhn, in: 2021 3rd International Conference on Management Science and Industrial Engineering, ACM, New York, NY, USA, 2021, pp. 192–199.

Advanced Data Analytics Platform for Manufacturing Companies
T. Voigt, N. Migenda, M. Schöne, D. Pelkmann, M. Fricke, W. Schenck, M. Kohlhase, in: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), IEEE, 2021, pp. 01–08.

Least Squares Approach for Multivariate Split Selection in Regression Trees
M. Schöne, M. Kohlhase, in: C. Analide, P. Novais, D. Camacho, H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, Springer International Publishing, Cham, 2020, pp. 41–50.

Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees
M. Schöne, M. Kohlhase, in: H. Schulte, F. Hoffmann, R. Mikut (Eds.), Proceedings - 30. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, 2020.