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Prof. dr. S. (Sebastian) Schelter

Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Informatics Institute

Bezoekadres
  • Science Park 900
Postadres
  • Postbus 94323
    1090 GH Amsterdam
Contactgegevens
  • Publicaties

    2024

    • Deng, S., Sprangers, O., Li, M., Schelter, S., & de Rijke, M. (2024). Domain Generalization in Time Series Forecasting. ACM Transactions on Knowledge Discovery from Data, 18(5), Article 113. https://doi.org/10.1145/3643035 [details]

    2023

    2022

    • 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 [details]
    • Ariannezhad, M., Yahya, M., Meij, E., Schelter, S., & de Rijke, M. (2022). Understanding Financial Information Seeking Behavior from User Interactions with Company Filings. In WWW '22 Companion: companion proceedings of the Web Conference 2022: April 25, 2022, Lyon, France (pp. 586-594). Association for Computing Machinery. https://doi.org/10.1145/3487553.3524636 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]

    2021

    • Ariannezhad, M., Jullien, S., Nauts, P., Fang, M., Schelter, S., & de Rijke, M. (2021). Understanding Multi-Channel Customer Behavior in Retail. In CIKM '21: proceedings of the 30th ACM International Conference on Information & Knowledge Management : November 1-5, 2021, virtual event, Australia (pp. 2867–2871). The Association for Computing Machinery. https://doi.org/10.1145/3459637.3482208 [details]
    • Grafberger, S., Guha, S., Stoyanovich, J., & Schelter, S. (2021). MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines. In SIGMOD '21: proceedings of the 2021 International Conference on the Management of Data : June 20 -25, 2021, virtual event, China (pp. 2736–2739). Association for Computing Machinery. https://doi.org/10.1145/3448016.3452759 [details]
    • Kersbergen, B., & Schelter, S. (2021). Learnings from a Retail Recommendation System on Billions of Interactions at bol.com. In 2021 IEEE 37th International Conference on Data Engineering: ICDE 2021 : proceedings : Chania, Greece, 19-22 April 2021 (pp. 2447-2452). (International Conference on Data Engineering; Vol. 37). IEEE Computer Society. https://doi.org/10.1109/ICDE51399.2021.00277 [details]
    • Schelter, S. (2021). Letter from the Special Issue Editor. Bulletin of the Technical Committee on Data Engineering, 44(1), 2. http://sites.computer.org/debull/A21mar/p2.pdf [details]
    • Schelter, S., Grafberger, S., & Dunning, T. (2021). HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. In SIGMOD '21: proceedings of the 2021 International Conference on the Management of Data : June 20 -25, 2021, virtual event, China (pp. 1545–1557). Association for Computing Machinery. https://doi.org/10.1145/3448016.3457239 [details]
    • Sprangers, O., Schelter, S., & de Rijke, M. (2021). Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression. In KDD ’21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining : August 14-18, 2021, virtual event, Singapore (pp. 1510-1520). Association for Computing Machinery. https://doi.org/10.1145/3447548.3467278 [details]

    2020

    2023

    2023

    2022

    2021

    • Doehmen, T., Mühleisen, H. F., Raasveldt, M., & Schelter, S. (2021). Data Quality Assertions for Machine Learning Pipeline. Paper presented at Workshop on Challenges in Deploying and Monitoring ML Systems at ICML.
    • Grafberger, S., Stoyanovich, J., & Schelter, S. (2021). Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines. Paper presented at Conference on Innovative Data Systems Research (CIDR) 2020.
    • Grafberger, S., Stoyanovich, J., & Schelter, S. (2021). Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines. Paper presented at Conference on Innovative Data Systems Research. http://cidrdb.org/cidr2021/papers/cidr2021_paper27.pdf
    • Redyuk, S., Kaoudi, Z., Markl, V., & Schelter, S. (2021). Automating Data Quality Validation for Dynamic Data Ingestion. Paper presented at International Conference on Extending Database Technology (EDBT 2021).
    • Schelter, S. (2021). Towards Efficient Machine Unlearning via Incremental View Maintenance. Paper presented at Workshop on Challenges in Deploying and Monitoring ML Systems at ICML. https://ssc.io/pdf/ivm-unlearning.pdf
    • Schelter, S., Rukat, T., & Biessmann, F. (2021). Jenga - A Framework to Study the Impact of Data Errors on the Predictions of Machine Learning Models. Paper presented at International Conference on Extending Database Technology (EDBT 2021).
    • Wang, L., & Schelter, S. (2021). Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items. Paper presented at Workshop on Online Recommender Systems and User Modeling at ACM RecSys. https://doi.org/10.48550/arXiv.2201.13313

    2020

    2024

    • Sprangers, O. R. (2024). Efficient and accurate forecasting in large-scale settings. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2023

    • Ariannezhad, M. (2023). User-oriented recommender systems in retail. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2024

    This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list.
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