Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 47
Number of items: 47
  • 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
  • Open Access
    Zhang, Z., Groth, P., Calixto, I., & Schelter, S. (2024). Directions Towards Efficient and Automated Data Wrangling with Large Language Models. In 2024 IEEE 40th International Conference on Data Engineering Workshops: ICDEW 2024 : 13-17 May 2024, Utrecht, Netherlands : proceedings (pp. 301-304). IEEE Computer Society. https://doi.org/10.1109/ICDEW61823.2024.00044
  • Open Access
    Sprangers, O., Wadman, W., Schelter, S., & de Rijke, M. (2024). Hierarchical forecasting at scale. International Journal of Forecasting, 40(4), 1689-1700. https://doi.org/10.1016/j.ijforecast.2024.02.006
  • Open Access
    Sprangers, O. R. (2024). Efficient and accurate forecasting in large-scale settings. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Sarvi, F., Vardasbi, A., Aliannejadi, M., Schelter, S., & de Rijke, M. (2023). On the Impact of Outlier Bias on User Clicks. In SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 23-27, 2023, Taipei, Taiwan (pp. 18-27). Association for Computing Machinery. https://doi.org/10.1145/3539618.3591745
  • Open Access
    Sarvi, F., Aliannejadi, M., Schelter, S., & de Rijke, M. (2023). How to Make an Outlier? Studying the Effect of Presentational Features on the Outlierness of Items in Product Search Results. In CHIIR'23: proceedings of the 2023 Conference on Human Information Interaction and Retrieval : March 19-23, 2023, Austin, Texas, USA (pp. 346-350). The Association for Computing Machinery. https://doi.org/10.1145/3576840.3578278
  • Open Access
    Guha, S., Khan, F. A., Stoyanovich, J., & Schelter, S. (2023). Automated Data Cleaning Can Hurt Fairness in Machine Learning-based Decision Making. In 2023 IEEE 39th International Conference on Data Engineering: ICDE 2023 : proceedings : 3-7 April 2023, Anaheim, California (pp. 3747-3754). IEEE Computer Society. https://doi.org/10.1109/ICDE55515.2023.00303
  • Open Access
    Grafberger, S., Guha, S., Groth, P., & Schelter, S. (2023). Mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses over and Over? Proceedings of the VLDB Endowment, 16(12), 4002–4005. https://doi.org/10.14778/3611540.3611606
  • 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
    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
Page 2 of 5