| 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 | 59 | 2018 |
| " Deep" Learning for Missing Value Imputationin 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 | 28 | 2018 |
| Quantifying interpretability and trust in machine learning systems P Schmidt, F Biessmann arXiv preprint arXiv:1901.08558, 2019 | 25 | 2019 |
| 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 | 8 | 2019 |
| Differential Data Quality Verification on Partitioned Data S Schelter, S Grafberger, P Schmidt, T Rukat, M Kiessling, A Taptunov, ... 2019 IEEE 35th International Conference on Data Engineering (ICDE), 1940-1945, 2019 | 7 | 2019 |
| Unit testing data with deequ S Schelter, F Biessmann, D Lange, T Rukat, P Schmidt, S Seufert, ... Proceedings of the 2019 International Conference on Management of Data, 1993 …, 2019 | 4 | 2019 |
| Transparency and trust in artificial intelligence systems P Schmidt, F Biessmann, T Teubner Journal of Decision Systems 29 (4), 260-278, 2020 | 2 | 2020 |
| Deequ-Data Quality Validation for Machine Learning Pipelines S Schelter, S Grafberger, P Schmidt, T Rukat, M Kiessling, A Taptunov, ... | 1 | 2018 |
| Calibrating Human-AI Collaboration: Impact of Risk, Ambiguity and Transparency on Algorithmic Bias P Schmidt, F Biessmann International Cross-Domain Conference for Machine Learning and Knowledge …, 2020 | | 2020 |
Felix BiessmannEinstein Center Digital Future, Beuth UniversityVerified email at beuth-hochschule.de
