| Transparency and trust in artificial intelligence systems P Schmidt, F Biessmann, T Teubner Journal of Decision Systems 29 (4), 260-278, 2020 | 258 | 2020 |
| 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 |
| Quantifying interpretability and trust in machine learning systems P Schmidt, F Biessmann arXiv preprint arXiv:1901.08558, 2019 | 134 | 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 | 114 | 2019 |
| " 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 | 90 | 2018 |
| Calibrating human-ai collaboration: Impact of risk, ambiguity and transparency on algorithmic bias P Schmidt, F Biessmann Machine Learning and Knowledge Extraction: 4th IFIP TC 5, TC 12, WG 8.4, WG …, 2020 | 35 | 2020 |
| Automated data validation in machine learning systems F Biessmann, J Golebiowski, T Rukat, D Lange, P Schmidt | 30 | 2021 |
| 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 | 25 | 2019 |
| Deequ-data quality validation for machine learning pipelines S Schelter, P Schmidt, T Rukat, M Kiessling, A Taptunov, F Biessmann, ... | 23 | 2018 |
| 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 | 20 | 2019 |
| Transparency P Schmidt The Sage Encyclopedia of Surveillance, Security and Privacy. Los Angeles: Sage, 2018 | 16 | 2018 |
| Data integration for toxic comment classification: Making more than 40 datasets easily accessible in one unified format J Risch, P Schmidt, R Krestel Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), 157-163, 2021 | 15 | 2021 |
| Quantifying Interpretability and Trust in Machine Learning Systems.(2019) P Schmidt, F Biessmann Citado na, 57, 1901 | 9 | 1901 |
| Quantifying interpretability and trust in machine learning systems. arXiv 2019 P Schmidt, F Biessmann arXiv preprint arXiv:1901.08558, 0 | 6 | |
| More than words: Towards better quality interpretations of text classifiers MB Zafar, P Schmidt, M Donini, C Archambeau, F Biessmann, SR Das, ... arXiv preprint arXiv:2112.12444, 2021 | 5 | 2021 |
| Learning action embeddings for off-policy evaluation M Cief, J Golebiowski, P Schmidt, Z Abedjan, A Bekasov European Conference on Information Retrieval, 108-122, 2024 | 3 | 2024 |
| Generating Domain-Specific Knowledge Graphs: Challenges with Open Information Extraction. N Jain, AS Múnera, M Lomaeva, J Streit, S Thormeyer, P Schmidt, ... TEXT2KG/MK@ ESWC, 52-69, 2022 | 3 | 2022 |
| Variational boosted soft trees T Cinquin, T Rukat, P Schmidt, M Wistuba, A Bekasov International Conference on Artificial Intelligence and Statistics, 5787-5801, 2023 | 1 | 2023 |
| Guiding Catalogue Enrichment with User Queries Y Du, J Golebiowski, P Schmidt, Z Abedjan Joint European Conference on Machine Learning and Knowledge Discovery in …, 2024 | | 2024 |
| More Than Words: Towards Better Quality Interpretations of Text Classifiers M Bilal Zafar, P Schmidt, M Donini, C Archambeau, F Biessmann, ... arXiv e-prints, arXiv: 2112.12444, 2021 | | 2021 |
Felix BiessmannEinstein Center Digital Future, Berlin University of Applied SciencesVerified email at bht-berlin.de
