| Challenges in the Deployment and Operation of Machine Learning in Practice. L Baier, F Jöhren, S Seebacher ECIS 1, 2019 | 205 | 2019 |
| Human vs. supervised machine learning: Who learns patterns faster? N Kühl, M Goutier, L Baier, C Wolff, D Martin Cognitive Systems Research 76, 78-92, 2022 | 50 | 2022 |
| How to conduct rigorous supervised machine learning in information systems research: the supervised machine learning report card N Kühl, R Hirt, L Baier, B Schmitz, G Satzger Communications of the Association for Information Systems 48 (1), 46, 2021 | 50 | 2021 |
| How to cope with change?-preserving validity of predictive services over time L Baier, N Kühl, G Satzger | 37 | 2019 |
| Detecting concept drift with neural network model uncertainty L Baier, T Schlör, J Schöffer, N Kühl arXiv preprint arXiv:2107.01873, 2021 | 35 | 2021 |
| Handling concept drift for predictions in business process mining L Baier, J Reimold, N Kühl 2020 IEEE 22nd Conference on Business Informatics (CBI) 1, 76-83, 2020 | 31 | 2020 |
| Handling Concept Drifts in Regression Problems--the Error Intersection Approach L Baier, M Hofmann, N Kühl, M Mohr, G Satzger arXiv preprint arXiv:2004.00438, 2020 | 29 | 2020 |
| Will the customers be happy? Identifying unsatisfied customers from service encounter data L Baier, N Kühl, R Schüritz, G Satzger Journal of Service Management 32 (2), 265-288, 2021 | 27 | 2021 |
| Conceptualizing Digital Resilience for AI-based Information Systems. M Schemmer, D Heinz, L Baier, M Vössing, N Kühl ECIS, 2021 | 20 | 2021 |
| Switching scheme: a novel approach for handling incremental concept drift in real-world data sets L Baier, V Kellner, N Kühl, G Satzger arXiv preprint arXiv:2011.02738, 2020 | 8 | 2020 |
| Utilizing concept drift for measuring the effectiveness of policy interventions: The case of the COVID-19 pandemic L Baier, N Kühl, J Schöffer, G Satzger arXiv preprint arXiv:2012.03728, 2020 | 5 | 2020 |
| Utilizing adaptive ai-based information systems to analyze the effectiveness of policy measures in the fight of covid-19 L Baier, J Schöffer, N Kühl | 3 | 2020 |
| Concept Drift Handling in Information Systems: Preserving the Validity of Deployed Machine Learning Models L Baier | 2 | 2021 |
| Increasing Robustness for Machine Learning Services in Challenging Environments: Limited Resources and No Label Feedback L Baier, N Kühl, J Schmitt Intelligent Systems and Applications: Proceedings of the 2021 Intelligent …, 2022 | | 2022 |
| Concept Drift Handling in Information Systems: Preserving the Validity of Deployed Machine Learning Models L Baier | | |
