Dr. ing. S. (Sebastian) Schelter
Faculty of Science
Informatics Institute
Visiting address
- Science Park 904
Postal address
- Postbus 94323
1090 GH Amsterdam
Contact details
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Publications
2022
- 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]
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). http://sites.computer.org/debull/A21mar/issue1.htm
- 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, [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 [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 [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. Database Engineering Bulletin, 43(4), 39-50. http://sites.computer.org/debull/A20dec/p39.pdf [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 [9] HILDA. https://ssc.io/publication/fairness-aware-instrumentation-of-preprocessing-pipelines-forml-hilda20/ [details]
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://orsum.inesctec.pt/orsum2021/assets/files/paper1.pdf
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/
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Ancillary activities
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Ahold Delhaize
Research Fellow (Director of Engineering and Forecasting)
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Ahold Delhaize
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.