1.
Groeneveld, J., Herrmann, J., Mollenhauer, N., Dreessen, L., Bessin, N., Schulze-Tast, J., Kastius, A., Huegle, J., Schlosser, R.: Self-Learning Agents for Recommerce Markets. Business & Information Systems Engineering 66 (4). 441–463 (2024).
2.
Huegle, J., Hagedorn, C., Schlosser, R.: A kNN-based Non-Parametric Conditional Independence Test for Mixed Data and Application in Causal Discovery. ECML-PKDD 2023, accepted (2023).
3.
Braun, T., Hurdelhey, B., Meier, D., Tsayun, P., Hagedorn, C., Huegle, J., Schlosser, R.: GPUCSL: GPU-Based Library for Causal Structure Learning. ICDM Open Project Forum. pp. 1236–1239 (2022).
4.
Hagedorn, C., Lange, C., Huegle, J., Schlosser, R.: GPU Acceleration for Information-theoretic Constraint-based Causal Discovery. In: Le, T.D., Liu, L., Kıcıman, E., Triantafyllou, S., and Liu, H. (eds.) Proceedings of The KDD’22 Workshop on Causal Discovery, Proceedings of Machine Learning Research (PMLR) 185. pp. 30–60 (2022).
5.
Hagedorn, C., Huegle, J., Schlosser, R.: Understanding Unforeseen Production Downtimes in Manufacturing Processes using Log Data-driven Causal Reasoning. Journal of Intelligent Manufacturing. 33, 2027–2043 (2022).
6.
Huegle, J., Hagedorn, C., Perscheid, M., Plattner, H.: MPCSL - A Modular Pipeline for Causal Structure Learning. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. pp. 3068–3076. Association for Computing Machinery, New York, NY, USA (2021).
7.
Huegle, J., Hagedorn, C., Uflacker, M.: Unterstützte Fehlerbehebung durch kausales Strukturwissen in Überwachungssystemen der Automobilfertigung. In: Götz, S., Linsbauer, L., Schaefer, I., and Wortmann, A. (eds.) Software Engineering 2021 Satellite Events, Lecture Notes in Informatics (LNI). Gesellschaft für Informatik, Bonn (2021).
8.
Hagedorn, C., Huegle, J.: GPU-Accelerated Constraint-Based Causal Structure Learning for Discrete Data. Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). pp. 37–45 (2021).
9.
Huegle, J.: An Information-Theoretic Approach on Causal Structure Learning for Heterogeneous Data Characteristics of Real-World Scenarios. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. pp. 4891–4892. International Joint Conferences on Artificial Intelligence Organization (2021).
10.
Hagedorn, C., Huegle, J.: Constraint-Based Causal Structure Learning in Multi-GPU Environments. In: Seidl, T., Fromm, M., and Obermeier, S. (eds.) Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021. pp. 106–118. CEUR-WS.org (2021).
11.
Huegle, J., Hagedorn, C., Boehme, L., Poerschke, M., Umland, J., Schlosser, R.: MANM-CS: Data Generation for Benchmarking Causal Structure Learning from Mixed Discrete-Continuous and Nonlinear Data. WHY-21 @ NeurIPS 2021 (2021).
12.
Matthies, C., Huegle, J., Dürschmid, T., Teusner, R.: Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices. In: Felderer, M., Hasselbring, W., Rabiser, R., and Jung, R. (eds.) Software Engineering 2020. pp. 73–74. Gesellschaft für Informatik e.V., Bonn (2020).
13.
Huegle, J., Hagedorn, C., Uflacker, M.: How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. pp. 5246–5248. International Joint Conferences on Artificial Intelligence Organization (2020).
14.
Schmidt, C., Huegle, J.: Towards a GPU-Accelerated Causal Inference. HPI Future SOC Lab - Proceedings 2017. pp. 187–194 (2020).
15.
Schmidt, C., Huegle, J., Horschig, S., Uflacker, M.: Out-of-Core GPU-Accelerated Causal Structure Learning. Algorithms and Architectures for Parallel Processing. ICA3PP 2019. pp. 89–104. Springer International Publishing (2020).
16.
Schmidt, C., Huegle, J., Bode, P., Uflacker, M.: Load-Balanced Parallel Constraint-Based Causal Structure Learning on Multi-Core Systems for High-Dimensional Data. SIGKDD Workshop on Causal Discovery. pp. 59–77 (2019).
17.
Hesse, G., Matthies, C., Glass, K., Huegle, J., Uflacker, M.: Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems. IEEE International Conference on Distributed Computing Systems (ICDCS). pp. 1381–1392 (2019).
18.
Matthies, C., Huegle, J., Dürschmid, T., Teusner, R.: Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET). pp. 158–169. IEEE (2019).
19.
Schmidt, C., Huegle, J., Uflacker, M.: Order-independent constraint-based causal structure learning for gaussian distribution models using GPUs. SSDBM ’18 Proceedings of the 30th International Conference on Scientific and Statistical Database Management. pp. 19:1–19:10. ACM, New York, NY, USA (2018).