Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms. - Sina Däubener; Sebastian Schmitt; Hao Wang 025; Peter Krause 001; Thomas Bäck (ICDM, 2019)

Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks. - Markus Thill; Sina Däubener; Wolfgang Konen; Thomas Bäck (ITAT, 2019)

Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification. - Sina Däubener; Lea Schönherr; Asja Fischer; Dorothea Kolossa (INTERSPEECH, 2020)

Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification. - Sina Däubener; Asja Fischer (CoRR, 2020)

Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing. - Denis Lukovnikov; Sina Däubener; Asja Fischer (EMNLP (Findings), 2021)

SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations. - Arne P. Raulf; Sina Däubener; Ben Hack; Axel Mosig; Asja Fischer (ESANN, 2021)

Approaches to Uncertainty Quantification in Federated Deep Learning. - Florian Linsner; Linara Adilova; Sina Däubener; Michael Kamp; Asja Fischer (PKDD/ECML Workshops (1), 2021)

How Sampling Impacts the Robustness of Stochastic Neural Networks. - Sina Däubener; Asja Fischer (NeurIPS, 2022)

On the Limitations of Model Stealing with Uncertainty Quantification Models. - David Pape; Sina Däubener; Thorsten Eisenhofer; Antonio Emanuele Cinà; Lea Schönherr (ESANN, 2023)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

Integrating uncertainty quantification into randomized smoothing based robustness guarantees. - Sina Däubener; Kira Maag; David Krueger 001; Asja Fischer (CoRR, 2024)

Leveraging stochasticity to increase the robustness of artificial neural networks. - Sina Däubener (2024)

ELBO, regularized maximum likelihood, and their common one-sample approximation for training stochastic neural networks. - Sina Däubener; Simon Damm; Asja Fischer (UAI, 2025)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Clara Meister; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; et al. (Trans. Mach. Learn. Res., 2025)