Publications
Here you find the publications of the research group AI-Systems Engineering. The current publications of the scientists associated with InES can be found in the respective homepages of the associated chairs:
- Assistant Professor for Management Analytics, Prof. Dr. Jana-Rebecca Rehse
- Data Web and Science Group
- Chair of Enterprise Systems, Prof. Dr. Hartmut Höhle
- Chair of General Management and Information Systems, Prof. Dr. Armin Heinzl
- Chair of Information Systems II, Prof. Dr. Christian Becker
- Chair of Logistics and Supply Chain Management, Prof. Dr. Moritz Fleischmann
- Chair of Production Management, Prof. Dr. Raik Stolletz
Publications of the Research Group AI-Systems Engineering
- Klerings, A., Bartelt, C. and Mueller, A. (2024). Developmentally plausible multimodal language models are highly modular. In , The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning : proceedings of the Second BabyLM Challenge : November 15–16, 2024 (S. 118–139). , Association for Computational Linguistics: Miami, FL, USA.
- Kolthoff, K., Bartelt, C., Ponzetto, S. P. and Schneider, K. (2024). Self-elicitation of requirements with automated GUI prototyping.
In , Proceedings of the 39th IEEE/
ACM International Conference on Automated Software Engineering : ASE 2024 : October 28 – November 1, 2024, Sacramento, California, USA (S. 2354-2357). , IEEE/ ACM: Sacramento, CA, USA. - Kolthoff, K., Kretzer, F., Bartelt, C., Maedche, A. and Ponzetto, S. P. (2024). Interlinking user stories and GUI prototyping: A semi-automatic LLM-based approach. In , 2024 IEEE 32nd International Requirements Engineering Conference (RE) (S. 1–9). , IEEE: Reykjavik, Iceland.
- Marton, S., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2024). GRANDE: Gradient-Based Decision Tree Ensembles for tabular data. In , International Conference on Learning Representations (S. 1–27). , OpenReview.net: .
- 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.
- Oesterle, M., Grams, T. and Bartelt, C. (2024). DRAMA at the PettingZoo: Dynamically restricted action spaces for multi-agent reinforcement learning frameworks. In , Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024, Hilton Hawaiian Village Waikiki Beach Resort, Hawaii, USA, January 3–6, 2024 (S. 7810-7819). , Department of IT-Management, Shidler College of Business, University of Hawaii: Honolulu, HI.
- Oesterle, M., Grams, T., Bartelt, C. and Stuckenschmidt, H. (2024). RAISE the bar: Restriction of action spaces for improved social welfare and equity in traffic management. In , Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (S. 1492-1500). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.
- Tschalzev, A., Marton, S., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2024). A data-centric perspective on evaluating machine learning models for tabular data. In , The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (S. 1–35). , NeurIPS: Vancouver, BC.
- Tschalzev, A., Nitschke, P., Kirchdorfer, L., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2024). Enabling mixed effects neural networks for diverse, clustered data using Monte Carlo methods. In , Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence: Jeju, 03–09 August 2024 (S. ). , International Joint Conferences on Artificial Intelligence: Jeju, South Korea.
- Wilken, N., Cohausz, L., Bartelt, C. and Stuckenschmidt, H. (2024). Fact Probability Vector Based Goal Recognition. In , 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) (S. 4254-4261). Frontiers in Artificial Intelligence and Applications, IOS Press: Amsterdam [u. a.].
- 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.
- Ernst, J. S., Marton, S., Brinkmann, J., Vellasques, E., Foucard, D., Kraemer, M. and Lambert, M. (2023). Bias mitigation for large language models using adversarial learning. In , Proceedings of the 1st Workshop on Fairness and Bias in AI co-located with 26th European Conference on Artificial Intelligence (ECAI 2023),Kraków, Poland, October 1st, 2023 (S. 1–14). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- He, Y., Schreckenberger, C., Stuckenschmidt, H. and Wu, X. (2023). Towards utilitarian online learning – A review of online algorithms in open feature space. In , Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (S. 6647-6655). , International Joint Conferences on Artificial Intelligence Organization: Macao SAR.
- 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.].
- Marton, S., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2023). GradTree: Learning axis-aligned decision trees with gradient descent. In , (S. 1–17). , Neural Information Processing Systems Foundation, Inc. (NeurIPS): New Orleans.
- 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.].
- Cohausz, L., Wilken, N. and Stuckenschmidt, H. (2022). Plan-similarity based heuristics for goal recognition. In , 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) : PerCom Workshops 2022 (S. 316–321). , IEEE: Pisa.
- 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. (2021). Self-learning governance of competitive multi-agent systems. In , Organic Computing : Doctoral Dissertation Colloquium 2020 (S. 47–63). Intelligent Embedded Systems, Kassel University Press: Kassel.
- Pernpeintner, M. (2021). Toward a self-learning governance loop for competitive multi-attribute MAS. In , AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems : Richland, SC, Virtual Event United Kingdom, May, 2021 (S. 1619-1621). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.
- 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. and Stuckenschmidt, H. (2021). Combining symbolic and statistical knowledge for goal recognition in smart home environments. In , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (S. 26–31). , IEEE Computer Society: Piscataway, NJ.
- 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. (2020). Automatic generation of graphical user interface prototypes from unrestricted natural language requirements.
In , 2019 34th IEEE/
ACM International Conference on Automated Software Engineering : 10–15 November 2019 : San Diego, California : proceedings (S. 1234-1237). , IEEE: Piscataway, NJ. - 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. - Pernpeintner, M. (2020). Achieving emergent governance in competitive multi-agent systems. In , AAMAS '20 : Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, Auckland, Nea Zealand, May 2020 (S. 2204-2206). , ACM Digital Library: New York, NY.
- Schindler, C. (2020). Towards transparency-encouraging partial software disclosure to enable trust in data usage.
In , ASE 2020 : 35th IEEE/
ACM International Conference on Automated Software Engineering : Mon 21 – Fri 25 September 2020, Melbourne, Australia : Doctoral Symposium (S. 1–3). , Association for Computing Machinery: 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.].
- Wieber, N. (2020). Automated generation of client-specific backends utilizing existing microservices and architectural knowledge.
In , ASE 2020 : 35th IEEE/
ACM International Conference on Automated Software Engineering : Mon 21 – Fri 25 September 2020, Melbourne, Australia : Doctoral Symposium (S. 1–3). , Association for Computing Machinery: New York, NY. - 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.
- Pernpeintner, M. (2019). Collaboration as an emergent property of self-organizing software systems. In , 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems : FAS*W 2019 : proceedings (S. 231–233). , IEEE Computer Society Press: Piscataway, NJ.
- 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.
- Achichi, M., Cheatham, M., Dragisic, Z., Euzenat, J., Faria, D., Ferrara, A., Flouris, G., Fundulaki, I., Harrow, I., Ivanova, V., Jiménez-Ruiz, E., Kolthoff, K., Kuss, E., Lambrix, P., Leopold, H., Li, H., Meilicke, C., Mohammadi, M., Montanelli, S., Pesquita, C., Saveta, T., Shvaiko, P., Splendiani, A., Stuckenschmidt, H., Thiéblin, E., Todorov, K., Trojahn, C. and Zamazal, O. (2017). Results of the Ontology Alignment Evaluation Initiative 2017. In , OM 2017 : Proceedings of the 12th International Workshop on Ontology Matching co-located with the 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, October 21, 2017 (S. 61–113). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Burzlaff, F. (2017). Knowledge-driven architecture composition. In , 47. Jahrestagung der Gesellschaft für Informatik, Informatik 2017, Chemnitz, Germany, September 25–29, 2017 (S. 2365-2370). Lecture Notes in Informatics, Gesellschaft für Informatik e. V.: Bonn.
- 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.
- Atkinson, C., Gerbig, R. and Metzger, N. (2015). On the execution of deep models.
In , EXE 2015 : Proceedings of the 1st International Workshop on Executable Modeling co-located with ACM/
IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS 2015) Ottawa, Canada, September 27, 2015 (S. 28–33). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany. - De Loera, J. A., Margulies, S., Pernpeintner, M., Riedl, E., Rolnick, D., Spencer, G., Stasi, D. and Swenson, J. (2015). Graph coloring ideals : Nullstellensatz certificates, Gröbner bases for chordal graphs, and hardness of Gröbner bases. In , ISSAC '15 : proceedings of the 2015 ACM International Symposium on Symbolic and Algebraic Computation : July 6–9, 2015, Bath, United Kingdom (S. 133–140). , ACM: New York, NY.
- Kolthoff, K. and Dutta, A. (2015). Semantic relation composition in large scale knowledge bases. In , Linked Data for Information Extraction : Proceedings of the Third International Workshop on Linked Data for Information Extraction (LD4IE2015) co-loc. with the 14th International Semantic Web Conference (ISWC 2015) ; Bethlehem, PA, USA, Oct. 12, 2015 (S. 34–47). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Cohausz, L., Tschalzev, A., Bartelt, C. and Stuckenschmidt, H. (2024). Investigating demographic features and their connection to performance, predictions, and fairness in EDM models. Journal of Educational Data Mining, 16, 177–213.
- Marton, S., Lüdtke, S., Bartelt, C., Tschalzev, A. and Stuckenschmidt, H. (2024). Explaining neural networks without access to training data. Machine Learning, 113, 3633-3652.
- 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.
- Umlauft, J., Johnson, C. W., Roux, P., Trugman, D. T., Lecointre, A., Walpersdorf, A., Nanni, U., Gimbert, F., Rouet-Leduc, B., Hulbert, C., Lüdtke, S., Marton, S. and Johnson, P. A. (2023). Mapping glacier basal sliding applying machine learning. Journal of geophysical research : JGR. F, Earth surface, 128, 1–20.
- 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.
- Klingert, S., Wilken, N. and Becker, C. (2020). Sim2Win: How simulation can help data centers to benefit from controlling their power profile. Energy Efficiency, 13, 1007-1029.
- Marton, S., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2024). GradTree: Learning axis-aligned decision trees with gradient descent. The 38th Annual AAAI Conference on Artificial Intelligence, Vancouver, Canada.
- 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.
- Metzger, N. and Weil, S. (2019). DFG-Projekt : Optimierter Einsatz von OCR-Verfahren – Tesseract als Komponente im OCR-D-Workflow. Workshop der Heidelberger MAD|HD-Gruppe, Heidelberg, Germany.