| Next place prediction: a systematic literature review C Schreckenberger, S Beckmann, C Bartelt Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human …, 2018 | 16 | 2018 |
| Online random feature forests for learning in varying feature spaces C Schreckenberger, Y He, S Lüdtke, C Bartelt, H Stuckenschmidt AAAI Press, 2023 | 7 | 2023 |
| Restructuring of hoeffding trees for trapezoidal data streams C Schreckenberger, T Glockner, H Stuckenschmidt, C Bartelt 2020 International Conference on Data Mining Workshops (ICDMW), 416-423, 2020 | 7 | 2020 |
| iDropout: Leveraging deep taylor decomposition for the robustness of deep neural networks C Schreckenberger, C Bartelt, H Stuckenschmidt On the Move to Meaningful Internet Systems: OTM 2019 Conferences …, 2019 | 5 | 2019 |
| Towards utilitarian online learning-A review of online algorithms in open feature space Y He, C Schreckenberger, H Stuckenschmidt, X Wu Proc. of International Joint Conferences on Artificial Intelligence Organization, 2023 | 4 | 2023 |
| Enhancing a crowd-based delivery network with mobility predictions C Schreckenberger, C Bartelt, H Stuckenschmidt Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction …, 2019 | 3 | 2019 |
| Dynamic forest for learning from data streams with varying feature spaces C Schreckenberger, C Bartelt, H Stuckenschmidt International Conference on Cooperative Information Systems, 95-111, 2022 | 2 | 2022 |
| Robust decision tree induction from unreliable data sources C Schreckenberger, C Bartelt, H Stuckenschmidt CEUR Workshop Proceedings 2655, Paper 6, 1-8, 2020 | | 2020 |
| Tree-based learning for dynamic data streams C Schreckenberger, C Bartelt, H Stuckenschmidt | | 2020 |
