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10 Publications


2024 | Journal Article | LibreCat-ID: 54548
R. P. Prager and H. Trautmann, “Exploratory Landscape Analysis for Mixed-Variable Problems,” IEEE Transactions on Evolutionary Computation, pp. 1–1, 2024, doi: 10.1109/TEVC.2024.3399560.
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2024 | Conference Paper | LibreCat-ID: 56277
S. Kilsbach and N. Michel, “Computer-Based Generation of Learner-Sensitive Feedback to Argumentative Learner Texts,” presented at the Tenth Conference of the International Society for the Study of Argumentation, Leiden, 2024.
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2023 | Book Chapter | LibreCat-ID: 52662
M. Nachtigall, M. Schlichtig, and E. Bodden, “Evaluation of Usability Criteria Addressed by Static Analysis Tools on a Large Scale,” in Software Engineering 2023, Bonn: Gesellschaft für Informatik e.V., 2023, pp. 95–96.
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2023 | Conference Paper | LibreCat-ID: 52816
I. Gräßler and M. Hieb, “Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing,” in Lectures, Nuremberg, 2023, pp. 253–524, doi: 10.5162/smsi2023/d7.4.
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2022 | Conference Paper | LibreCat-ID: 32410
M. Nachtigall, M. Schlichtig, and E. Bodden, “A Large-Scale Study of Usability Criteria Addressed by Static Analysis Tools,” in Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, 2022, pp. 532–543, doi: 10.1145/3533767.
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2021 | Journal Article | LibreCat-ID: 21004
M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.
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2021 | Book Chapter | LibreCat-ID: 48881
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–15.
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2020 | Conference Paper | LibreCat-ID: 48897
M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in Parallel Problem Solving from {Nature} (PPSN XVI), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.
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2018 | Conference Paper | LibreCat-ID: 3852 | OA
M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
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2018 | Journal Article | LibreCat-ID: 48884
P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging TSP Solver Complementarity through Machine Learning,” Evolutionary Computation, vol. 26, no. 4, pp. 597–620, 2018, doi: 10.1162/evco_a_00215.
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