2026

• Berger, U., Biehler, R., Binder, K., Elmer, C., Ertz, F., Hotz, T., Huber, S., Ickstadt, K., Kauermann, G., Küchenhoff, H., Lübke, K., Münnich, R., Schüller, K., Skill, T., Weihs, C., Weinert, H., & Weisser, C. (2026). Daten, Künstliche Intelligenz und Evidenz – neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung. AStA Wirtschafts- und Sozialstatistisches Archiv. https://doi.org/10.1007/s11943-026-00372-0
• Cebada-Aleu, A., Sauer, S., & Weisser, C. (2026). Von der KI-Plattform zur dialogfähigen Intelligenz: Die Zukunft industrieller Prognosen. In A. Gillhuber, G. Kauermann, & W. Hauner (Eds.), Künstliche Intelligenz und Data Science in Theorie und Praxis: Von klassischen Algorithmen zu Generativer KI und Agentic AI – Konzepte und Realisierung in Unternehmen (pp. 397–409). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-662-73474-2_26
• Kumar, M., Thielmann, A. F., Weisser, C., & Säfken, B. (2026). From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning. Transactions on Machine Learning Research. https://openreview.net/pdf?id=str7wQt9Qc
• Lukassen, F., Herrmann, J., Weisser, C., Silbersdorff, A., Säfken, B., & Kneib, T. (2026). Quality Without Usefulness: LLM-Generated XAI Narratives as Trust Heuristics Rather Than Decision Aids. arXiv. https://arxiv.org/abs/2605.26770
• Lukassen, F., Herrmann, J., Weisser, C., Säfken, B., & Kneib, T. (2026). From XAI to Stories: A Factorial Study of LLM-Generated Explanation Quality. arXiv. https://arxiv.org/abs/2601.02224
• Lukassen, F., Weisser, C., Kneib, T., & Silbersdorff, A. (2026). CAFE: A Compound-AI Factorial Evaluation Framework. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026): System Demonstrations. In Press. https://arxiv.org/abs/2607.10380
• Lukassen, F., Weisser, C., Schlee, M., Kumar, M., Thielmann, A., Säfken, B., Silbersdorff, A., & Kneib, T. (2026). LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanation. arXiv. https://arxiv.org/abs/2601.02957
• Lukassen, F., Weisser, C., & Silbersdorff, A. (2026). Cheap, Consistent, Calibrated, but Biased? Typed Decision Models as Judges. arXiv. https://arxiv.org/abs/2610.18122
• Paredes Amorin, A., Python, A., & Weisser, C. (2026). Not All News Is Equal: Topic- and Event-Conditional Sentiment from Finetuned LLMs for Aluminum Price Forecasting. In M. El-Haj, A. Moreno Sandoval, A. Garcia-Serrano, C.-C. Chen, P. Rayson, Y. A. Torterolo Orta, P. Martinez, & J. Porta (Eds.), Proceedings of the 7th Financial Narrative Processing Workshop (FNP 2026) at LREC 2026 (pp. 59–77). Palma de Mallorca, Spain: European Language Resources Association (ELRA). https://doi.org/10.63317/59x9shp32g6h
• Schiller, R. P., Weisser, C., Müller, K.-R., Ochsenfeld, C., & Semnani, P. (2026). TAME: Element-wise Mixture-of-Experts Fusion for Data-Efficient and Interpretable Molecular Property Prediction. ChemRxiv. Accepted for presentation at the SIMBIOCHEM and AI4Mat Workshops at NeurIPS 2026. https://doi.org/10.26434/chemrxiv.15008270/v1
• Schlee, M., Lukassen, F., & Weisser, C. (2026). LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification. arXiv. https://arxiv.org/abs/2608.11753
• Schlee, M., Weisser, C., Kivimäki, T., Mashiku, M., & Säfken, B. (2026). LabelFusion: Fusing Large Language Models with Transformer Encoders for Robust Financial News Classification. In M. El-Haj, A. Moreno Sandoval, A. Garcia-Serrano, C.-C. Chen, P. Rayson, Y. A. Torterolo Orta, P. Martinez, & J. Porta (Eds.), Proceedings of the 7th Financial Narrative Processing Workshop (FNP 2026) at LREC 2026 (pp. 98–105). Palma de Mallorca, Spain: European Language Resources Association (ELRA). https://doi.org/10.63317/5k4dk6iczjq5
• Schultenkämper, S., & Bäumer, F. S. (2026). Information Disclosure on the Web: Developing a Threat Model for the Digital Twin. In Proceedings of the 2026 International Conference on Semantic Computing (ICSC) (pp. 366–371). https://doi.org/10.1109/ICSC67292.2026.00061
• Schultenkämper, S., & Bäumer, F. S. (2026). Synthetic Personas for Social Networks: Census Demographics with LLM-Generated Behavior. In International Conference on Information and Software Technologies (ICIST 2026). Springer. In Press.
• Vovchenko, V., Barberi, V., Schultenkämper, S., & Bäumer, F. S. (2026). ADRIAN InstructFace-Edit – Towards Robust Detection of AI-Manipulated Face Images. In The First International Conference on Security and Cybersecurity in the AI and Digital Context. DTR Society Press. In Press.
• Vovchenko, V., Schultenkämper, S., & Bäumer, F. S. (2026). A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models. In AIMEDIA 2026: The Second International Conference on AI-based Media Innovation. IARIA. In Press.