Dr. ing. S. (Sebastian) Schelter
Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Informatics Institute
Bezoekadres
- Science Park 904
Postadres
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Postbus 94323
1090 GH Amsterdam
Contactgegevens
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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. Advance online publication. https://doi.org/10.1145/3643035
2023
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 [details]- Grafberger, S., Groth, P., & Schelter, S. (2023). Provenance Tracking for End-to-End Machine Learning Pipelines. In ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023 (pp. 1512). Association for Computing Machinery, Inc. https://doi.org/10.1145/3543873.3587557
- 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 [details]
- 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 [details]
- 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 [details]
- 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 [details]
- 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 [details]
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
- Anil, R., Capan, G., Drost-Fromm, I., Dunning, T., Friedman, E., Grant, T., Quinn, S., Ranjan, P., Schelter, S., & Yılmazel, Ö. (2020). Apache Mahout: Machine Learning on Distributed Dataflow Systems. Journal of Machine Learning Research, 21, Article 127. https://jmlr.csail.mit.edu/papers/v21/18-800.html [details]
- Ariannezhad, M., Schelter, S., & de Rijke, M. (2020). Demand Forecasting in the Presence of Privileged Information. In V. Lemaire, S. Malinowski, A. Bagnall, T. Guyet, R. Tavenard, & G. Ifrim (Eds.), Advanced Analytics and Learning on Temporal Data: 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020 : revised selected papers (pp. 46-62). (Lecture Notes in Computer Science; Vol. 12588), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-65742-0_4 [details]
- Hendriksen, M., Kuiper, E., Nauts, P., Schelter, S., & de Rijke, M. (2020). Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers. In The 2020 SIGIR Workshop On eCommerce: July 30 : accepted papers Article 23 SIGIR eCom'20. https://sigir-ecom.github.io/ecom20Papers/paper23.pdf [details]
- Sarvi, F., Voskarides, N., Mooiman, L., Schelter, S., & de Rijke, M. (2020). A Comparison of Supervised Learning to Match Methods for Product Search. In The 2020 SIGIR Workshop On eCommerce: July 30 : accepted papers Article 30 SIGIR eCom'20. https://sigir-ecom.github.io/ecom20Papers/paper30.pdf [details]
- Schelter, S. (2020). Technical Perspective: Query Optimization for Faster Deep CNN Explanations. SIGMOD Record, 49(1), 60. https://doi.org/10.1145/3422648.3422662 [details]
- Schelter, S., & Stoyanovich, J. (2020). Taming Technical Bias in Machine Learning Pipelines. Bulletin of the Technical Committee on Data Engineering, 43(4), 39-50. http://sites.computer.org/debull/A20dec/p39.pdf [details]
- Schelter, S., Rukat, T., & Biessmann, F. (2020). Learning to Validate the Predictions of Black Box Classifiers on Unseen Data. In SIGMOD '20: proceedings of the 2020 ACM SIGMOD International Conference on Management of Data : June 14-19, 2020, Portland, OR, USA (pp. 1289-1299). Association for Computing Machinery. https://doi.org/10.1145/3318464.3380604 [details]
- Yang, K., Huang, B., Stoyanovich, J., & Schelter, S. (2020). Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning. In HILDA 2020: Workshop on Human-In-the-Loop Data Analytics : co-located with SIGMOD 2020 : 19 June 2020, Portland, OR, USA Article 9 HILDA. https://ssc.io/publication/fairness-aware-instrumentation-of-preprocessing-pipelines-forml-hilda20/ [details]
2023
- 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 [details]
2023
- Grafberger, S., Karlaš, B., Groth, P. T., & Schelter, S. (2023). Towards Declarative Systems for Data-Centric Machine Learning. Abstract from Data-Centric Machine Learning Research work-
shop (DMLR) at ICML. https://dmlr.ai/assets/accepted-papers/41/CameraReady/autodc.pdf
2022
- Schelter, S., Grafberger, S., Guha, S., Sprangers, O., Karlaš, B., & Zhang, C. (2022). Screening Native Machine Learning Pipelines with ArgusEyes. Abstract from Conference on Innovative Data Systems Research 2022, Chaminade, California, United States. https://ssc.io/publication/screening-native-ml-pipelines-with-arguseyes-cidr/
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
- Jullien, S., Schelter, S., & de Rijke, M. (2020). RetaiL: Open Your Own Grocery Store to Reduce Waste. Paper presented at Advances in Neural Information Processing Systems 2020. https://ssc.io/publication/retail-open-your-own-grocery-store-to-reduce-waste-neurips-demo/
2024
- Döhmen, T., Geacu, R., Hulsebos, M. & Schelter, S. (5-4-2024). SchemaPile: A Large Collection of Relational Database Schemas. Zenodo. https://doi.org/10.1145/3654975
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. -
Nevenwerkzaamheden
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Ahold Delhaize
Research Fellow (Director of Engineering and Forecasting)
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Ahold Delhaize
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.