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Results: 12
Number of items: 12
  • 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
    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
    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., 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
    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
    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
    Grafberger, S., Groth, P., Stoyanovich, J., & Schelter, S. (2022). Data distribution debugging in machine learning pipelines. VLDB Journal, 31(5), 1103-1126. https://doi.org/10.1007/s00778-021-00726-w
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