Publications

Kobschall, K., Hartung, L., Kramer, S. (2025). Adaptive differentiable trees for transparent learning on data streams. MACHINE LEARNING, 114(11). DOI Author/Publisher URL

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 (Vols 15669, pp. 318-329). DOI Author/Publisher URL

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

Cerrato, M., Köppel, M., Esposito, R., Kramer, S. (2023). Invariant Representations with Stochastically Quantized Neural Networks. In B. Williams, Y. Chen, and 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