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Results: 47
Number of items: 47
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2023). Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines. Proceedings of the ACM on Management of Data, 1(2), Article 128. https://doi.org/10.1145/3589273
  • Open Access
    Ariannezhad, M. (2023). User-oriented recommender systems in retail. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2023). Provenance Tracking for End-to-End Machine Learning Pipelines. In The ACM Web Conference 2023: Companion of the World Wide Web Conference WWW 2023 : April 30-May 4, 2023, Austin, Texas, USA (pp. 1512). Association for Computing Machinery. https://doi.org/10.1145/3543873.3587557
  • Döhmen, T., Hulsebos, M., Becks, C., & Schelter, S. (2022). GitSchemas: A Dataset for Automating Relational Data Preparation Tasks. In Proceedings, 2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW 2022): 9-11 May 2022, virtual event (pp. 74-78). IEEE Computer Society. https://doi.org/10.1109/ICDEW55742.2022.00016
  • Sarvi, F., Heuss, M., Aliannejadi, M., Schelter, S., & de Rijke, M. (2022). Understanding and Mitigating the Effect of Outliers in Fair Ranking. In WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining : February 21-25, 2022 : virtual event, Tempe, AZ, USA (pp. 861-869). Association for Computing Machinery. https://doi.org/10.1145/3488560.3498441
  • Ariannezhad, M., Jullien, S., Li, M., Fang, M., Schelter, S., & de Rijke, M. (2022). ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping. In SIGIR '22: proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 11-15, 2022, Madrid, Spain (pp. 1240-1250). The Association for Computing Machinery. https://doi.org/10.1145/3477495.3531708
  • Redyuk, S., Kaoudi, Z., Schelter, S., & Markl, V. (2022). DORIAN in action: Assisted Design of Data Science Pipelines. Proceedings of the VLDB Endowment, 15(12), 3714–3717. https://doi.org/10.14778/3554821.3554882
  • Kersbergen, B., Sprangers, O., & Schelter, S. (2022). Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale. In SIGMOD '22: proceedings of the 2022 International Conference on the Management of Data : June 12-17, 2022, Philadelphia, PA, USA (pp. 150-159). Association for Computing Machinery. https://doi.org/10.1145/3514221.3517901
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2022). Towards data-centric what-if analysis for native machine learning pipelines. In Proceedings of the Sixth Workshop on Data Management for End-to-End Machine Learning: in conjunction with the 2022 ACM SIGMOD/PODS Conference, Philadelphia, PA, USA Article 3 Association for Computing Machinery. https://doi.org/10.1145/3533028.3533303
  • Open Access
    Stoyanovich, J., Abiteboul, S., Howe, B., Jagadish, H. V., & Schelter, S. (2022). Responsible data management. Communications of the ACM, 65(6), 64-74. https://doi.org/10.1145/3488717
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