Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 48
Number of items: 48
  • Open Access
    Schelter, S., Grafberger, S., Guha, S., Karlaš, B., & Zhang, C. (2023). Proactively Screening Machine Learning Pipelines with ArgusEyes. In SIGMOD '23 Companion: Companion of the 2023 ACM/SIGMOD International Conference on Management of Data : June 18-23, 2023, Seattle, WA, USA (pp. 91–94). Association for Computing Machinery. https://doi.org/10.1145/3555041.3589682
  • 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
    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
  • Open Access
    Sprangers, O., Schelter, S., & de Rijke, M. (2023). Parameter Efficient Deep Probabilistic Forecasting. International Journal of Forecasting, 39(1), 332-345. https://doi.org/10.1016/j.ijforecast.2021.11.011
  • 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
  • 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
  • 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
    Schelter, S. (2022). Letter from the Special Issue Editor. Bulletin of the Technical Committee on Data Engineering, 45(1), 2-3. http://sites.computer.org/debull/A22mar/p2.pdf
Page 3 of 5