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Results: 48
Number of items: 48
  • Open Access
    Zhang, Z., Groth, P., Calixto, I., & Schelter, S. (2025). A Deep Dive Into Cross-Dataset Entity Matching with Large and Small Language Models. In A. Simitsis, B. Kemme, A. Queralt, O. Romero, & P. Jovanovic (Eds.), Proceedings 28th International Conference on Extending Database Technology, EDBT 2025, Barcelona, Spain, March 25-28, 2025 (pp. 922-934). (Advances in Database Technology; Vol. 28, No. 3). OpenProceedings.org. https://doi.org/10.48786/EDBT.2025.75
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2025). mlidea: Interactively Improving ML Data Preparation Code via "Shadow Pipelines". Proceedings of the VLDB Endowment, 18(12), 5359–5362. https://doi.org/10.14778/3750601.3750671
  • Open Access
    Grafberger, S. (2025). Declarative machine learning pipeline management via logical query plans. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Sarvi, F. (2025). Learning to rank for e-commerce search. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Kersbergen, B. (2025). Expanding boundaries in scalable session-based recommendations. [Thesis, fully internal, Universiteit van Amsterdam].
  • Döhmen, T., Radu, G., Hulsebos, M., & Schelter, S. (2024, July 7). SchemaPile: A Large Collection of Relational Database Schemas [Data set]. Zenodo. https://doi.org/10.5281/zenodo.12682521
  • Open Access
    Schelter, S., Grafberger, S., & de Rijke, M. (2024). Snarcase - Regain Control over Your Predictions with Low-Latency Machine Unlearning. Proceedings of the VLDB Endowment, 17(12), 4273-4276. https://doi.org/10.14778/3685800.3685853
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2024). Towards Interactively Improving ML Data Preparation Code via “Shadow Pipelines”. In Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning (DEEM): in conjunction with the 2024 ACM SIGMOD/PODS Conference, Santiago, Chile (pp. 7–11). The Association for Computing Machinery. https://doi.org/10.1145/3650203.3663327
  • Open Access
    Sprangers, O. R. (2024). Efficient and accurate forecasting in large-scale settings. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Redyuk, S., Kaoudi, Z., Schelter, S., & Markl, V. (2024). Assisted design of data science pipelines. The VLDB Journal, 33(4), 1129-1153. https://doi.org/10.1007/s00778-024-00835-2
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