From Cleaning before ML to Cleaning for ML F Neutatz, B Chen, Z Abedjan, E Wu IEEE Data Engineering Bulletin 44, 24-41, 2021 | 42 | 2021 |
ED2: A Case for Active Learning in Error Detection F Neutatz, M Mahdavi, Z Abedjan CIKM, 2249-2252, 2019 | 36 | 2019 |
Towards Automated Data Cleaning Workflows M Mahdavi, F Neutatz, L Visengeriyeva, Z Abedjan Proceedings of the Conference on Learning. Knowledge. Data. Analytics., 10-19, 2019 | 33 | 2019 |
Automated Feature Engineering for Algorithmic Fairness R Salazar Diaz, F Neutatz, Z Abedjan Proceedings of the VLDB Endowment 14 (9), 1694-1702, 2021 | 32 | 2021 |
HALEF: An Open-Source Standard-Compliant Telephony-Based Modular Spoken Dialog System: A Review and An Outlook D Suendermann-Oeft, V Ramanarayanan, M Teckenbrock, F Neutatz, ... Natural Language Dialog Systems and Intelligent Assistants, 53-61, 2015 | 32 | 2015 |
Data Cleaning and AutoML: Would an optimizer choose to clean? F Neutatz, B Chen, Y Alkhatib, J Ye, Z Abedjan Datenbank-Spektrum, 2022 | 18 | 2022 |
Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study F Neutatz, F Biessmann, Z Abedjan Proceedings of the 2021 International Conference on Management of Data, 2021 | 8 | 2021 |
Data Science für alle: Grundlagen der Datenprogrammierung: Ein Data-Science-Kurs für alle Studierenden der TU Berlin Z Abedjan, H Anuth, M Esmailoghli, M Mahdavi, F Neutatz, B Chen Informatik Spektrum 43, 129-136, 2020 | 6 | 2020 |
AutoML in heavily constrained applications F Neutatz, M Lindauer, Z Abedjan The VLDB Journal, 1-23, 2023 | 1 | 2023 |
Evaluating Acoustic, Textual and Grammar Features for Alcohol Classification F Neutatz, D Schmidt, M Teckenbrock, D Suendermann-Oeft ESSV, 2016 | 1 | 2016 |
What is “Good” Training Data? - Data Quality Dimensions that Matter for Machine Learning F Neutatz, Z Abedjan Künstliche Intelligenz - Wie gelingt eine vertrauenswürdige Verwendung in …, 2022 | | 2022 |