Publications

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

Köbschall, K., Hartung, L., and Kramer, S. (2025). Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams. In Lecture Notes in Computer Science (pp. 167-182). Springer Nature Switzerland. DOI

Pensel, L., and Kramer, S. (2025). Neural RELAGGS. MACHINE LEARNING, 114(5). 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

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

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

Derstroff, C., Cerrato, M., Brugger, J., et al. (2023). Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations. CoRR, abs/2312.09950. DOI Author/Publisher URL

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

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

Kramer, S., Cerrato, M., Dzeroski, S., King, R. D. (2023). Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems. CoRR, abs/2305.02251. 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

Pensel, L., Kramer, S. (2022). Neural RELAGGS. Author/Publisher URL

Hauptmann, T., Kramer, S. (2022). A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction. Author/Publisher URL

Garcon, A., Vexler, J., Budker, D., Kramer, S. (2022). Deep neural networks to recover unknown physical parameters from oscillating time series. PLOS ONE, 17(5). DOI Author/Publisher URL

Koeppel, M., Segner, A., Wagener, M., et al. (2022). Learning to rank Higgs boson candidates. SCIENTIFIC REPORTS, 12(1). DOI Author/Publisher URL