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

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

Siekiera, J., Köppel, M., Simpson, E., et al. (2022). Ranking Creative Language Characteristics in Small Data Scenarios. In M. M. Hedblom, A. A. Kantosalo, R. Confalonieri, et al. (eds.), Proceedings of the 13th International Conference on Computational Creativity, Bozen-Bolzano, Italy, June 27 - July 1, 2022 (pp. 136-140). Association for Computational Creativity (ACC). Author/Publisher URL