• 2022

    Research article in proceedings (conference)

    Pohl, J. S., Assenmacher, D., Seiler, M. V., Trautmann, H., & Grimme, C. (2022). Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In Association, f. t. A. o. A. I. (. (Ed.), Workshop Proceedings of the 16th International Conference on Web and Social Media (ICWSM) (pp. 1–10). Palo Alto, CA, USA: AAAI Press.
    More details BibTeX Full text DOI

    Prager, R. P., Seiler, M. V., Trautmann, H., & Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 3–17). Cham: Springer International Publishing.
    More details BibTeX Full text DOI

    Seiler, M. V., Prager, R. P., Kerschke, P., & Trautmann, H. (2022). A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In -, (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (pp. 657–665). New York, NY, USA: Association for Computing Machinery.
    More details BibTeX DOI

    Research article (journal)

    Heins, J., Bossek, J., Pohl, J. S., Seiler, M. V., Trautmann, H., & Kerschke, P. (2022). A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection. Theoretical Computer Science (Theoret. Comput. Sci.), 940.
    More details BibTeX DOI

    Forschungsartikel in Online-Sammlung

    Pohl, J. S., Seiler, M. V., Assenmacher, D., & Grimme, C. (2022). A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022.
    More details Full text DOI

  • 2021

    Research article in proceedings (conference)

    Assenmacher, D., Niemann, M., Müller, K., Seiler, M. V., Riehle, D. M., & Trautmann, H. (2021). RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), Virtual Event, 1–14.
    More details BibTeX Full text

    Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In Association, f. C. M. (Ed.), Proceedings of the 16th ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI) (pp. 1–15). Dornbirn, Austria: Association for Computing Machinery.
    More details BibTeX Full text DOI

    Prager, R. P., Moritz, V. H., & Pascal, (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Orlando, Florida, USA.
    More details BibTeX

  • 2020

    Research article in proceedings (conference)

    Clever, L., Assenmacher, D., Müller, K., Seiler, M. V., Riehle, D. M., Preuss, M., & Grimme, C. (2020). FakeYou! — A Gamified Approach for Building and Evaluating Resilience Against Fake News. In Proceedings of the 2nd Multidisciplinary International Symposium on Disinformation in Open Online Media, Leiden, Netherlands.
    More details BibTeX DOI

    Seiler, M. V., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 48–64.
    More details BibTeX Full text DOI

    Seiler, M. V., Trautmann, H., & Kerschke, P. (2020). Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 1–8.
    More details BibTeX Full text DOI