Schwerpunkte/Kompetenzen
- Analyse und Prognose von Zeitreihen
- Nicht-lineare Regressionsmodelle
- Change-point Analyse
- Machine Learning Methoden
Publikationen
- Sicks, R.; Korn, R.; Schwaar, S.:
A Generalised Linear Model Framework for β-Variational Autoencoders based on Exponential Dispersion Families.
Journal of Machine Learning Research, Volume 22, Pages 1-41, (2021). - Blandfort, F.; Glock, C.; Sass, J.; Sefrin, S.; Schwaar, S.:
Efficient and Comprehensive Time-Dependent Reliability Analysis of Complex Structures by a Parameter State Model.
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(2), (2021). - Franke, J.; Hefter M.; Herzwurm A.; Ritter K.; Schwaar, S.:
Adaptive Quantile Computation for Brownian Bridge in Change-Point Analysis.
(akzeptiert in Computational Statistics and Data Analysis, https://authors.elsevier.com/c/1dzVRcBz8QCXn), (2020). - Schwaar, S.:
Data-driven Change-Point Test and Estimator.
https://arxiv.org/abs/2010.12449, (2020). - Sicks, R.; Korn R.; Schwaar S.:
A lower bound for the ELBO of the Bernoulli Variational Autoencoder. https://arxiv.org/abs/2003.11830, (2020). - Blandfort, F.; Glock, C.; Sass, J.; Sefrin, S.; Schwaar, S.:
Subset Simulation Interpolation - A New Approach to Compute Effects of Model-Dynamics in Structural Reliability.
29th European Safety and Reliability Conference (ESREL2019), Hannover, (2019). - Blandfort, F.; Glock, C.; Sass, J.; Sefrin, S.; Schwaar, S.:
A Parametric State Space Model for Time-Dependent Reliability Analysis.
Accepted for 17th International Probabilistic Workshop (IPW2019), Edinburgh, (2019). - Dresvyanskiy, D.; Karaseva, T.; Mitrofanov, S.; Redenbach, C.; Makogin, V.; Spodarev, E.; Schwaar, S.:
Application of Clustering Methods to Anomaly Detection in Fibrous Media.
IOP Conference Series: Materials Science and Engineering, Vol. 537 (2), p.022001, (2019). - Schwaar, S.:
Asymptotics for change-point tests and change-point estimators.
Dissertation, TU Kaiserslautern, (2017).
Sammlung der Publikationen von Stefanie Schwaar in der Fraunhofer-Publica