| Reinforcement learning for port-Hamiltonian systems O Sprangers, R Babuška, SP Nageshrao, GAD Lopes IEEE transactions on cybernetics 45 (5), 1017-1027, 2014 | 55 | 2014 |
| Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression O Sprangers, S Schelter, M de Rijke KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge …, 2021 | 21 | 2021 |
| Serenade-low-latency session-based recommendation in e-commerce at scale B Kersbergen, O Sprangers, S Schelter Proceedings of the 2022 International Conference on Management of Data, 150-159, 2022 | 10 | 2022 |
| Screening native ml pipelines with “arguseyes” S Schelter, S Grafberger, S Guha, O Sprangers, B Karlaš, C Zhang Conference on Innovative Data Systems Research. CIDR, 2022 | 10 | 2022 |
| Parameter Efficient Deep Probabilistic Forecasting O Sprangers, S Schelter, M de Rijke International Journal of Forecasting, 2022 | 8 | 2022 |
| Embedding machine learning into passivity theory: a port-Hamiltonian approach OR Sprangers MSc thesis, TU Delft, Delft University of Technology, 2012 | 2 | 2012 |
| Serving low-latency session-based recommendations at bol. com B Kersbergen, O Sprangers, S Schelter Proceedings of the Dutch-Belgian Database Day, 2021 | 1 | 2021 |
| Hierarchical forecasting at scale O Sprangers, W Wadman, S Schelter, M de Rijke International Journal of Forecasting, 2024 | | 2024 |
| Domain Generalization in Time Series Forecasting S Deng, O Sprangers, M Li, S Schelter, M de Rijke ACM Transactions on Knowledge Discovery from Data 18 (5), 1-24, 2024 | | 2024 |
| ETUDE–Evaluating the Inference Latency of Session-Based Recommendation Models at Scale B Kersbergen, O Sprangers, F Kootte, S Guha, M de Rijke, S Schelter | | |
| ARGUSEYES: Screening Native Machine Learning Pipelines S Grafberger, S Guha, O Sprangers, S Schelter | | |
