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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) (pp. 3747-3754). IEEE Computer Society. https://doi.org/10.1109/ICDE55515.2023.00303
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.
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]
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]
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]
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., 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., 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]
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]
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/
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
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