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
2021
- 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]
- Jullien, S., Schelter, S., & de Rijke, M. (2020). RetaiL: Open Your Own Grocery Store to Reduce Waste. In NeurIPS 2020 NIPS.
- 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
- 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, .
- 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., 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).
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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
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].