Lucas Baier
Lucas Baier
Verified email at
Cited by
Cited by
Challenges in the Deployment and Operation of Machine Learning in Practice.
L Baier, F Jöhren, S Seebacher
ECIS 1, 2019
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
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
How to cope with change?-preserving validity of predictive services over time
L Baier, N Kühl, G Satzger
Detecting concept drift with neural network model uncertainty
L Baier, T Schlör, J Schöffer, N Kühl
arXiv preprint arXiv:2107.01873, 2021
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
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
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
Conceptualizing Digital Resilience for AI-based Information Systems.
M Schemmer, D Heinz, L Baier, M Vössing, N Kühl
ECIS, 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
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
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
Concept Drift Handling in Information Systems: Preserving the Validity of Deployed Machine Learning Models
L Baier
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
Concept Drift Handling in Information Systems: Preserving the Validity of Deployed Machine Learning Models
L Baier
The system can't perform the operation now. Try again later.
Articles 1–15