Christian Schreckenberger

Research Assistant

University of Mannheim
Institute for Enterprise Systems
L15, 1–6 – Room 416
68161 Mannheim

E-mail: christian.schreckenbergeruni-mannheim.de

Credit: Katrin Glückler

Research Interests

Data Mining from Dynamic Data Streams
Decision Tree-based Machine Learning

Short CV

Since 2017	Research Assistant at the Institute of Enterprise Systems
2014–2017	Master of Science in Business Informatics (Data and Web Science) at the University of Mannheim
2011–2014	Bachelor of Science in Business Informatics at the University of Mannheim

Marton, S., Lüdtke, S., Bartelt, C., Tschalzev, A. and Stuckenschmidt, H. (2024). Explaining neural networks without access to training data. Machine Learning, 1–20.
Rink, J., Tollens, F., Tschalzev, A., Bartelt, C., Heinzl, A., Hoffmann, J., Schoenberg, S. O., Marzina, A., Sandikci, V., Wiegand, C., Hoyer, C. and Szabo, K. (2024). Establishing an MSU service in a medium-sized German urban area — clinical and economic considerations. Frontiers in Neurolgy, 15, 1–9.
Kolthoff, K., Bartelt, C. and Ponzetto, S. P. (2023). Correction to: Data-driven prototyping via natural- language-based GUI retrieval. Automated Software Engineering, 30, 1–2.
Kolthoff, K., Bartelt, C. and Ponzetto, S. P. (2023). Data-driven prototyping via natural-language-based GUI retrieval. Automated Software Engineering, 30, 1–34.
Burzlaff, F., Wilken, N., Bartelt, C. and Stuckenschmidt, H. (2022). Semantic interoperability methods for smart service systems: A survey. IEEE Transactions on Engineering Management : EM, 69, 4052-4066.
Marton, S., Lüdtke, S. and Bartelt, C. (2022). Explanations for neural networks by neural networks. Applied Sciences, 12, 1–14.
Niemann, F., Lüdtke, S., Bartelt, C. and Ten Hompel, M. (2022). Context-aware human activity recognition in industrial processes. Sensors, 22, 1–14.

Marton, S., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2024). GradTree: Learning axis-aligned decision trees with gradient descent. In , Proceedings of the 38th AAAI Conference on Artificial Intelligence (S. 14323-14331). , AAAI Press: Washington, DC.
Brinkmann, J., Swoboda, P. and Bartelt, C. (2023). A multidimensional analysis of social biases in vision transformers. In , Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (S. 4914-4923). , IEEE: Paris, France.
Cohausz, L., Tschalzev, A., Bartelt, C. and Stuckenschmidt, H. (2023). Investigating the importance of demographic features for EDM-predictions. In , Proceedings of the 16th International Conference on Educational Data Mining (S. 125–136). , International Educational Data Mining Society: Bengaluru, India.
Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2023). Outlying aspect mining via sum-product networks. In , Advances in knowledge discovery and data mining: 27th Pacific-Asia Conference on knowledge discovery and data mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023 : proceedings. Part I (S. 27–38). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Schreckenberger, C., He, Y., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2023). Online random feature forests for learning in varying feature spaces. In , Proceedings of the 37th AAAI Conference on Artificial Intelligence. Vol. 4 (S. 4587-4595). , AAAI Press: Washington, DC.
Wilken, N., Cohausz, L., Bartelt, C. and Stuckenschmidt, H. (2023). Planning landmark based goal recognition revisited: Does using initial state landmarks make sense? In , KI 2023: Advances in Artificial Intelligence : 46th Conference on AI, Berlin, Germany, September 26–29, 20023, proceedings (S. 231–244). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2022). Exchangeability-aware sum-product networks. In , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, 23–29 July 2022 (S. 4864-4870). , International Joint Conferences on Artificial Intelligence Organization: Wien.
Oesterle, M., Bartelt, C., Lüdtke, S. and Stuckenschmidt, H. (2022). Self-learning governance of black-box multi-agent systems. In , Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XV : International Workshop, COINE 2022, virtual event, May 9, 2022, revised selected papers (S. 73–91). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Schreckenberger, C., Bartelt, C. and Stuckenschmidt, H. (2022). Dynamic forest for learning from data streams with varying feature spaces. In , Cooperative information systems : 28th International Conference, CoopIS 2022, Bozen-Bolzano, Italy, October 4–7, 2022, Proceedings (S. 95–111). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Wilken, N., Cohausz, L., Schaum, J., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2022). Leveraging planning landmarks for hybrid online goal recognition. In , International Conference on Automated Planning and Scheduling ICAPS (2022) : June 13–17, 2022, virtual (S. ). , CEUR Workshop Proceedings: Aachen.
Burzlaff, F. and Bartelt, C. (2021). Knowledge-driven architecture composition: Assisting the system integrator to reuse integration knowledge. In , ICWE 2021 : 21st International Conference on Web Engineering, Biarritz, France, May 18–21, 2021 ; proceedings (S. 305–319). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Hoffmann, L., Bartelt, C. and Stuckenschmidt, H. (2021). Knowledge injection via ML-based initialization of neural networks. In , Proceedings of the CIKM 2021 Workshops (CIKMW 2021) co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) : Gold Coast, Queensland, Australia, November 1–5,2021 (S. 1–6). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Kolthoff, K., Bartelt, C. and Ponzetto, S. P. (2021). Automated Retrieval of Graphical User Interface Prototypes from Natural Language Requirements. In , NLDB 2021 : 26th International Conference on Natural Language & Information Systems, Saarbrücken, Germany, June 23–25, 2021 ; proceedings (S. 376–384). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Metzger, N., Hoffmann, L., Bartelt, C., Stuckenschmidt, H., Wommer, M. and Bescos del Castillo, M. B. (2021). Towards trace-graphs for data-driven test case mining in the domain of automated driving. In , Third IEEE International Conference on Artificial Intelligence Testing: AITest 2021 : proceedings : 23–26 August 2021, online event (S. 41–48). , IEEE: Piscataway, NJ.
Nolte, F., Wilken, N. and Bartelt, C. (2021). Rendezvous delivery: Utilizing autonomous electric vehicles to improve the efficiency of last mile parcel delivery in urban areas. In , 2021 IEEE PerCom Workshops : PerAwareCity 2021, 6th IEEE Workshop on Pervasive Context-Aware Smart Cities and Intelligent Transportation Systems, March 22–26, 2021 in Kassel, Germany (S. 148–153). , IEEE Computer Society: Piscataway, NJ.
Pernpeintner, M., Bartelt, C. and Stuckenschmidt, H. (2021). Governing black-box agents in competitive multi-agent systems. In , Multi-Agent Systems : 18th European Conference, EUMAS 2021, virtual event, June 28–29, 2021, revised selected papers (S. 19–36). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Wilken, N., Stuckenschmidt, H. and Bartelt, C. (2021). Combining symbolic and data-driven methods for goal recognition. In , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (S. 428–429). , IEEE Computer Society: Piscataway, NJ.
Burzlaff, F., Bongarth, B., Grottker, S., Hammen, J. and Bartelt, C. (2020). MergePoint: A graphical web-app for merging HTTP-endpoints and IoT-platform models. In , 53rd Hawaii International Conference on System Sciences, HICSS 2020 : Maui, Hawaii, USA, January 7–10, 2020 (S. 1–10). Proceedings of the 53rd Hawaii International Conference on System Sciences, ScolarSpace: Honolulu, HI.
Kolthoff, K., Bartelt, C. and Ponzetto, S. P. (2020). GUI2WiRe: Rapid wireframing with a mined and large-scale GUI repository using natural language requirements. In , ASE 2020 : 35th IEEE/ACM International Conference on Automated Software Engineering : Mon 21 – Fri 25 September 2020, Melbourne, Australia : Tool Demonstrations (S. 1297-1301). , ACM: New York, NY.
Schreckenberger, C., Bartelt, C. and Stuckenschmidt, H. (2020). Robust decision tree induction from unreliable data sources. In , STAIRS 2020 : Proceedings of the 9th European Starting AI Researchers' Symposium 2020 co-located with 24th European Conference on Artificial Intelligence (ECAI 2020) Santiago Compostela, Spain, August, 2020 (S. Paper 6, 1–8). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Schreckenberger, C., Glockner, T., Stuckenschmidt, H. and Bartelt, C. (2020). Restructuring of Hoeffding trees for Trapezoidal Data Streams. In , 20th IEEE International Conference on Data Mining Workshops : 17–20 November 2020, Virtual Conference : Proceedings (S. 416–423). , IEEE: Los Alamitos, CA [u.a.].
Burzlaff, F., Ackel, M. and Bartelt, C. (2019). A mapping language for IoT device descriptions. In , 2019 IEEE 43rd Annual Computer Software and Applications Conference : 15–19 July 2019, Milwaukee, Wisconsin : proceedings (S. 115–120). , IEEE Computer Society: Piscataway, NJ.
Burzlaff, F. and Bartelt, C. (2019). A conceptual architecture for enabling future self-adaptive service systems. In , 52nd Hawaii International Conference on System Sciences, HICSS 2019, Grand Wailea, Maui, Hawaii, USA, January 8–11, 2019 (S. 1–10). , AISeL: Atlanta, GA.
Schreckenberger, C., Bartelt, C. and Stuckenschmidt, H. (2019). Enhancing a crowd-based delivery network with mobility predictions. In , PredictGIS'19 : Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility : Chicago, IL, USA, November 05, 2019 (S. 66–75). Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, ACM: New York, NY.
Schreckenberger, C., Bartelt, C. and Stuckenschmidt, H. (2019). iDropout: Leveraging deep taylor decomposition for the robustness of deep neural networks. In , On the Move to Meaningful Internet Systems: OTM 2019 Conferences : Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21–25, 2019, Proceedings (S. 113–126). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Schreckenberger, C., Beckmann, S. and Bartelt, C. (2019). Next place prediction: A systematic literature review. In , PredictGIS 2018 : Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility : ACM GIS 2018 Conference: November 6 – November 9, 2018, Seattle, Washington (S. 37–45). Proceedings of the 2Nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility, ACM: New York, NY.
Burzlaff, F. and Bartelt, C. (2018). I4.0-device integration: A qualitative analysis of methods and technologies utilized by system integrators: Implications for enginering future industrial internet of things system. In , 2018 IEEE 15th International Conference on Software Architecture companion : ICSA-C 2018 : proceedings : 30 April-4 May 2018, Seattle, Washington (S. 27–34). , IEEE: Piscataway, NJ.
Burzlaff, F., Bartelt, C. and Adler, L. (2018). Towards automating service matching for manufacturing systems: Exemplifying knowledge-driven architecture composition. In , 51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018), Stockholm, Sweden, 16–18 May 2018 (S. 707–713). Procedia CIRP, Elsevier ; Curran: Amsterdam ; Red Hook, NY.
Burzlaff, F., Bartelt, C. and Jacobs, S. (2018). Executing model-based software development for embedded I4.0 devices properly. In , MOD-WS 2018 : Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018 (S. 35–46). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Burzlaff, F., Bartelt, C. and Stuckenschmidt, H. (2018). Next steps in knowledge-driven architecture composition. In , LWDA 2018 : Proceedings of the Conference “Lernen, Wissen, Daten, Analysen” Mannheim, Germany, August 22–24, 2018 (S. 78–83). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Burzlaff, F. and Bartelt, C. (2017). Knowledge-driven architecture composition: Case-based formalization of integration knowledge to enable automated component coupling. In , ICSA 2017 : 2017 IEEE International Conference on Software Architecture : side track proceedings : proceedings : 3–7 April 2017, Gothenburg, Sweden (S. 108–111). , IEEE: Piscataway, NJ.

Marton, S., Bartelt, C. and Stuckenschmidt, H. (2020). Machine learning for converting Black-Box models to interpretable functions. PhD Forum, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, Online.
Schreckenberger, C., Bartelt, C. and Stuckenschmidt, H. (2020). Tree-based learning for dynamic data streams. PhD Forum, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, Online.