Müller, Emmanuel

Deep Semi-Supervised Anomaly Detection

Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 388

Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions

Wischnewski, M., Krämer, N., & Müller, E. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Human-Automation Interaction and SafetySocial Robot Interaction and HRI

Cited by 146

OutRules: A Framework for Outlier Descriptions in Multiple Context Spaces

Müller, E., Keller, F., Blanc, S., & Böhm, K. (2012) · Lecture Notes in Computer Science

Anomaly Detection Techniques and ApplicationsData Mining Algorithms and Applications

Cited by 7

Benchmarking Trust: A Metric for Trustworthy Machine Learning

Rutinowski, J., Klüttermann, S., Endendyk, J., Reining, C., & Müller, E. (2024) · Communications in Computer and Information Science

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 4

Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets

Balestra, C., Maj, C., Müller, E., & Mayr, A. (2023) · PLOS ONE

Bioinformatics and Genomic NetworksGame Theory and Voting Systems

Cited by 4

Human-in-the-Loop Oversight of AI is Compromised by Political Preferences

Dores Cruz, T. D., Starke, C., Katzke, T., Müller, E., Kwiatkowska, M., Lobel, O., Köbis, N., & Shalvi, S. (2025)

Ethics and Social Impacts of AIHuman-Automation Interaction and Safety

Cited by 0