• 2022

    Forschungsartikel in Sammelband (Konferenz)

    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.
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    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.
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    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: ACM Press.
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    Forschungsartikel (Zeitschrift)

    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.
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    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.
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  • 2021

    Forschungsartikel in Sammelband (Konferenz)

    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 Vanschoren, J., & Yeung, S. (Eds.), Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021) (pp. 1–14). online: Selbstverlag — Eigenverlag.
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    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: ACM Press.
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    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.
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  • 2020

    Forschungsartikel in Sammelband (Konferenz)

    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 van Duijn, , Max;, P., Mike;, S., Viktoria;, T., Frank;, V., & Suzan, (Eds.), Disinformation in Open Online Media. Second Multidisciplinary International Symposium, MISDOOM 2020, Leiden, The Netherlands, October 26–27, 2020, Proceedings (pp. 218–232). Lecture Notes in Computer Science: Vol. 12259. Cham: Springer.
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    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.
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    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.
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