Deep Learning in the Wild. - Thilo Stadelmann; Mohammadreza Amirian; Ismail Arabaci; Marek Arnold; Gilbert François Duivesteijn; Ismail Elezi; Melanie Geiger; Stefan Lörwald; Benjamin Bruno Meier; Katharina Rombach; Lukas Tuggener (ANNPR, 2018)

Automated Machine Learning in Practice: State of the Art and Recent Results. - Lukas Tuggener; Mohammadreza Amirian; Katharina Rombach; Stefan Lörwald; Anastasia Varlet; Christian Westermann; Thilo Stadelmann (SDS, 2019)

Improving generalization of deep fault detection models in the presence of mislabeled data. - Katharina Rombach; Gabriel Michau; Olga Fink (SMC, 2020)

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types. - Katharina Rombach; Gabriel Michau; Olga Fink (Sensors, 2021)

Controlled Generation of Unseen Faults for Partial and OpenSet&Partial Domain Adaptation. - Katharina Rombach; Gabriel Michau; Olga Fink (CoRR, 2022)

Contrastive Feature Learning for Fault Detection and Diagnostics in Railway Applications. - Katharina Rombach; Gabriel Michau; Kajan Ratnasabapathy; Lucian-Stefan Ancu; Wilfried Bürzle; Stefan Koller; Olga Fink (CoRR, 2022)

Supervised learning and reinforcement learning of feedback models for reactive behaviors: Tactile feedback testbed. - Giovanni Sutanto; Katharina Rombach; Yevgen Chebotar; Zhe Su; Stefan Schaal; Gaurav S. Sukhatme; Franziska Meier (Int. J. Robotics Res., 2022)

Controlled generation of unseen faults for Partial and Open-Partial domain adaptation. - Katharina Rombach; Gabriel Michau; Olga Fink (Reliab. Eng. Syst. Saf., 2023)

Interpretable prognostics with concept bottleneck models. - Florent Forest; Katharina Rombach; Olga Fink (Inf. Fusion, 2025)

Learning Informative Health Indicators Through Unsupervised Contrastive Learning. - Katharina Rombach; Gabriel Michau; Wilfried Bürzle; Stefan Koller; Olga Fink (IEEE Trans. Reliab., 2025)