Publications

Hauptmann, T., Tröbs, S.-O., Schulz, A., et al. (2025). Echocardiographic Measures Read by Artificial Intelligence Enable Accurate and Rapid Prediction of the Worsening of Heart Failure. European Heart Journal - Digital Health. Published online. DOI

Boer, D., Roth, S., Kramer, S. (2025). Focus, Merge, Rank: Improved Question Answering Based on Semi-structured Knowledge Bases. Author/Publisher URL

Pensel, L., Kramer, S. (2025). Neural RELAGGS. MACHINE LEARNING, 114(5). DOI Author/Publisher URL

Pensel, L., Kramer, S. (2025). Human Guided Learning of Transparent Regression Models. Author/Publisher URL

Beyer, A., Henkys, V., Kobus, R., et al. (2025). cuTeBool: Fast and Scalable Boolean Matrix Factorization on GPUs Using Tensor Cores. In Lecture Notes in Computer Science (pp. 249-264). Springer Nature Switzerland. DOI

Brugger, J., Pfanschilling, V., Richter, D., et al. (2025). Prompting Neural-Guided Equation Discovery Based on Residuals. In Lecture Notes in Computer Science (pp. 97-112). Springer Nature Switzerland. DOI

Koebschall, K., Hartung, L., Kramer, S. (2025). Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams (Vols 15243, pp. 167-182). DOI Author/Publisher URL

Stempel, K., Cerrato, M., Kramer, S. (2025). Exploring the Design Space of Fair Tree Learning Algorithms. In Lecture Notes in Computer Science (pp. 176-190). Springer Nature Switzerland. DOI

Vexler, J., Vieten, B., Nelke, M., Kramer, S. (2025). Integrating Inverse and Forward Modeling for Sparse Temporal Data from Sensor Networks. In Lecture Notes in Computer Science (pp. 318-329). Springer Nature Switzerland. DOI

Boer, D., Koch, F., and Kramer, S. (2024). Harnessing the Power of Semi-Structured Knowledge and LLMs with Triplet-Based Prefiltering for Question Answering. Author/Publisher URL

King, R. D., Scassa, T., Kramer, S., and Kitano, H. (2024). Stockholm declaration on AI ethics: why others should sign. Nature, 626(8000), 716-716. DOI

Derstroff, C., Brugger, J., Blüml, J., et al. (2024). Amplifying Exploration in Monte-Carlo Tree Search by Focusing on the Unknown. CoRR, abs/2402.08511. DOI Author/Publisher URL

Lerner, R., Baker, D., Schwitter, C., et al. (2023). Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples. NATURE COMMUNICATIONS, 14(1). DOI Author/Publisher URL

Hauptmann, T., Fellenz, S., Nathan, L., et al. (2023). Discriminative machine learning for maximal representative subsampling. SCIENTIFIC REPORTS, 13(1). DOI Author/Publisher URL

Lang, F., Sorn, P., Schrörs, B., et al. (2023). Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates. iScience, 26(11), 108014. DOI Author/Publisher URL

Vexler, J., Kramer, S. (2023). Classifying Aircraft Categories from Magnetometry Data Using a Hypotheses-Based Multi-Task Framework. In Frontiers in Artificial Intelligence and Applications. IOS Press. DOI

Vexler, J., Kramer, S. (2023). Identifying Aircraft Motions and Patterns from Magnetometry Data Using a Knowledge-Based Multi-Fusion Approach. 2023 26th International Conference on Information Fusion (FUSION), 1-8. DOI

Hauptmann, T., Kramer, S. (2023). A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction. BMC BIOINFORMATICS, 24(1). DOI Author/Publisher URL

Bammert, L.-M., Kramer, S., Cerrato, M., Althaus, E. (2023). Privacy-Preserving Learning of Random Forests Without Revealing the Trees. In A. Bifet, A. C. Lorena, R. P. Ribeiro, et al. (eds.), DS (Vols 14276, pp. 372-386). Springer. Author/Publisher URL

Cerrato, M., Köppel, M., Esposito, R., Kramer, S. (2023). Invariant Representations with Stochastically Quantized Neural Networks. In B. Williams, Y. Chen, J. Neville (eds.), Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023 (pp. 6962-6970). AAAI Press. DOI Author/Publisher URL