| The stratosphere platform for big data analytics A Alexandrov, R Bergmann, S Ewen, JC Freytag, F Hueske, A Heise, ... The VLDB Journal 23, 939-964, 2014 | 662 | 2014 |
| Automating large-scale data quality verification S Schelter, D Lange, P Schmidt, M Celikel, F Biessmann, A Grafberger Proceedings of the VLDB Endowment 11 (12), 1781-1794, 2018 | 250 | 2018 |
| On challenges in machine learning model management S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ... | 219 | 2015 |
| Probabilistic demand forecasting at scale JH Böse, V Flunkert, J Gasthaus, T Januschowski, D Lange, D Salinas, ... Proceedings of the VLDB Endowment 10 (12), 1694-1705, 2017 | 166 | 2017 |
| Automatically Tracking Metadata and Provenance of Machine Learning Experiments S Schelter, JH Boese, J Kirschnick, T Klein, S Seufert NIPS Workshop ML Systems, 2017 | 161 | 2017 |
| Elastic machine learning algorithms in amazon sagemaker E Liberty, Z Karnin, B Xiang, L Rouesnel, B Coskun, R Nallapati, ... Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 137 | 2020 |
| DataWig: Missing value imputation for tables F Biessmann, T Rukat, P Schmidt, P Naidu, S Schelter, A Taptunov, ... Journal of Machine Learning Research 20 (175), 1-6, 2019 | 114 | 2019 |
| Deep Learning for Missing Value Imputation in Tables with Non-Numerical Data F Biessmann, D Salinas, S Schelter, P Schmidt, D Lange Proceedings of the 27th ACM International Conference on Information and …, 2018 | 90 | 2018 |
| Scalable similarity-based neighborhood methods with mapreduce S Schelter, C Boden, V Markl Proceedings of the sixth ACM conference on Recommender systems, 163-170, 2012 | 81 | 2012 |
| Hedgecut: Maintaining randomised trees for low-latency machine unlearning S Schelter, S Grafberger, T Dunning Proceedings of the 2021 International Conference on Management of Data, 1545 …, 2021 | 76 | 2021 |
| "All roads lead to Rome": Optimistic recovery for distributed iterative data processing S Schelter, S Ewen, K Tzoumas, V Markl Proceedings of the 22nd ACM international conference on Information …, 2013 | 75 | 2013 |
| Collaborative filtering with apache mahout S Schelter, S Owen Proc. of ACM RecSys challenge, 2012 | 66 | 2012 |
| An intermediate representation for optimizing machine learning pipelines A Kunft, A Katsifodimos, S Schelter, S Breß, T Rabl, V Markl Proceedings of the VLDB Endowment 12 (11), 1553-1567, 2019 | 63 | 2019 |
| Distributed matrix factorization with mapreduce using a series of broadcast-joins S Schelter, C Boden, M Schenck, A Alexandrov, V Markl Proceedings of the 7th ACM Conference on Recommender Systems, 281-284, 2013 | 63 | 2013 |
| Fairprep: Promoting data to a first-class citizen in studies on fairness-enhancing interventions S Schelter, Y He, J Khilnani, J Stoyanovich arXiv preprint arXiv:1911.12587, 2019 | 57 | 2019 |
| On the Ubiquity of Web Tracking: Insights from a Billion-Page Web Crawl S Schelter, J Kunegis Journal of Web Science 4 (4), 53-66, 2018 | 53 | 2018 |
| Samsara: Declarative machine learning on distributed dataflow systems S Schelter, A Palumbo, S Quinn, S Marthi, A Musselman NIPS Workshop MLSystems, 2016 | 49 | 2016 |
| ReCANet: A repeat consumption-aware neural network for next basket recommendation in grocery shopping M Ariannezhad, S Jullien, M Li, M Fang, S Schelter, M de Rijke Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 48 | 2022 |
| Learning to validate the predictions of black box classifiers on unseen data S Schelter, T Rukat, F Bießmann Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 47 | 2020 |
| Apache mahout: Machine learning on distributed dataflow systems R Anil, G Capan, I Drost-Fromm, T Dunning, E Friedman, T Grant, S Quinn, ... Journal of Machine Learning Research 21 (127), 1-6, 2020 | 47 | 2020 |
Volker MarklTechnische Universität BerlinVerified email at tu-berlin.de
