| 2022 | PRDC | Automating Safety Argument Change Impact Analysis for Machine Learning Components. | Carmen Crlan, Lydia Gauerhof, Barbara Gallina, Simon Burton |
| 2021 | ISSRE | On the Necessity of Explicit Artifact Links in Safety Assurance Cases for Machine Learning. | Lydia Gauerhof, Roman Gansch, Christian Heinzemann, Matthias Woehrle, Andreas Heyl |
| 2020 | IJCAI | Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers. | Andrey Morozov, Emil Valiev, Michael Beyer, Kai Ding, Lydia Gauerhof, Christoph Schorn |
| 2020 | ISSRE | Considering Reliability of Deep Learning Function to Boost Data Suitability and Anomaly Detection. | Lydia Gauerhof, Yuki Hagiwara, Christoph Schorn, Mario Trapp |
| 2020 | WACV | Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection. | Lydia Gauerhof, Nianlong Gu |
| 2020 | SAFECOMP | Assuring the Safety of Machine Learning for Pedestrian Detection at Crossings. | Lydia Gauerhof, Richard Hawkins, Chiara Picardi, Colin Paterson, Yuki Hagiwara, Ibrahim Habli |
| 2020 | SAFECOMP | Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications. | Gesina Schwalbe, Bernhard Knie, Timo Smann, Timo Dobberphul, Lydia Gauerhof, Shervin Raafatnia, Vittorio Rocco |
| 2019 | SAFECOMP | Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions. | Simon Burton, Lydia Gauerhof, Bibhuti Bhusan Sethy, Ibrahim Habli, Richard Hawkins |
| 2018 | SAFECOMP | Structuring Validation Targets of a Machine Learning Function Applied to Automated Driving. | Lydia Gauerhof, Peter Munk, Simon Burton |
| 2017 | SAFECOMP | Making the Case for Safety of Machine Learning in Highly Automated Driving. | Simon Burton, Lydia Gauerhof, Christian Heinzemann |