Bild: Anna Logue
Bild: Katrin Glückler
Forschungsinteressen
- Data Mining aus Dynamischen Datenströmen
- Maschinelles Lernen mit Decision Trees
Lebenslauf
| Seit 2017 | Wissenschaftlicher Mitarbeiter am Institut für Enterprise Systems |
|---|---|
| 2014–2017 | Master of Science in Wirtschaftsinformatik (Data and Web Science) an der Universität Mannheim |
| 2011–2014 | Bachelor of Science in Wirtschaftsinformatik an der Universität Mannheim |
Publikationen
- He, Y., Schreckenberger, C., Stuckenschmidt, H. und 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.
- Schreckenberger, C., He, Y., Lüdtke, S., Bartelt, C. und 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.
- Schreckenberger, C., Bartelt, C. und 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.].
- Schreckenberger, C., Bartelt, C. und 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. und 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.].
- Schreckenberger, C., Bartelt, C. und 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. und 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. und 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.
- Schreckenberger, C., Bartelt, C. und 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.