Christian Knoll
Christian Knoll received his MSc (Dipl.Ing.) degree in Information and Computer Engineering in 2014 and earned his PhD degree im 2019 from Graz University of Technology. He is currently a postdoctoral researcher at Graz University of Technology.
His research interests include machine learning, graphical models, statistical signal processing, and statistical physics. He is particularly interested in applying message passing methods for probabilistic inference methods on graphical models.
Research Topics
Student Projects
- Anomaly Detection in Industrial Applications(open)
- Improving Probabilistic Inference(open)
- Oxygen Saturation Measurements for Apnea Divers
- Monitoring of Dairy Cows with Deep Learning
- Anomaly Detection in Dairy Cows
- Oxygen Measurements for Apnea Divers
- Changepoint Detection in Smartphone Usage
- Change Point Detection in Smartphone Usage
- Ultrasonic Inspection System(open)
- Alternative Descriptions for Random Variables
Publications
- Conference paper Toth C., Knoll C., Pernkopf F. & Peharz R. (2025) Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders. in 28th International Conference on Artificial Intelligence and Statistics, AISTATS 2025. [more info]
- Journal article Nagpal R., Cassiers G., Knoll C., Pernkopf F., Primas R. & Mangard S. (2025) On Loopy Belief Propagation for SASCAs: An Analysis and Empirical Study of the Inference Problem. in IACR Communications in Crypology, 1(4). [more info] [doi]
- Conference paper Leisenberger H., Knoll C. & Pernkopf F. (2024) Reliability Thresholds for the Bethe Free Energy Approximation. in 2nd SPIGM - ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling. [more info]
- Conference paper Toth C., Knoll C., Pernkopf F. & Peharz R. (2024) Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders. in 2nd SPIGM - ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling. [more info]
- Conference paper Steger S., Knoll C., Klein B., Fröning H. & Pernkopf F. (2024) Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles. in 2nd SPIGM - ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling. [more info]
- Conference paper Fuchs A., Knoll C., Leitinger E. & Pernkopf F. (2023) Self-attention for enhanced OAMP Detection in MIMO Systems. in 48th IEEE International Conference on Acoustics, Speech, and Signal Processing. [more info] [doi]
- Conference paper Knoll C. & Pernkopf F. (2023) Reliable Belief Propagation: Recent Theoretical and Practical Advances. in 33rd IEEE International Workshop on Machine Learning for Signal Processing. [more info] [doi]
- Conference paper Toth C., Lorch L., Knoll C., Krause A., Pernkopf F., Peharz R. & Kügelgen J. (2023) Active Bayesian Causal Inference. in 36th Conference on Neural Information Processing Systems. [more info]
- Journal article Knoll C., Weller A. & Pernkopf F. (2022) Self-Guided Belief Propagation – a Homotopy Continuation Method. in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, p. 1-18. [more info] [doi]
- Conference paper Leisenberger H., Pernkopf F. & Knoll C. (2022) Fixing the Bethe Approximation: How Structural Modifications in a Graph Improve Belief Propagation. in 38th Conference on Uncertainty in Artificial Intelligence (pp. 1085–1095). [more info]
- Conference paper Fuchs A., Knoll C. & Pernkopf F. (2021) Wasserstein Distribution Correction for Improved Robustness in Deep Neural Networks.. [more info]
- Conference paper Fuchs A., Knoll C. & Pernkopf F. (2021) Distribution Mismatch Correction for Improved Robustness in Deep Neural Networks. in Distribution Shifts. [more info]
- Conference paper Leisenberger H., Knoll C., Seeber R. & Pernkopf F. (2021) Convergence Behavior of Belief Propagation: Estimating Regions of Attraction via Lyapunov Functions. in 37th Conference on Uncertainty in Artificial Intelligence (pp. 1863-1873). [more info]
- Journal article Knoll C. & Pernkopf F. (2020) Belief propagation: accurate marginals or accurate partition function—where is the difference?. in Journal of Statistical Mechanics: Theory and Experiment, 2020(12). [more info] [doi]
- Doctoral Thesis Knoll C. (2019) Understanding the Behavior of Belief Propagation.. [more info]
- Conference paper Knoll C., Kulmer F. & Pernkopf F. (2019) Guided Selection of Accurate Belief Propagation Fixed Points. in Machine Learning and the Physical Sciences. [more info]
- Conference paper Knoll C. & Pernkopf F. (2019) Belief Propagation: Accurate Marginals or Accurate Partition Function - Where is the Difference?. in 2019 Conference on Uncertainty in Artificial Intelligence. [more info]
- Journal article Knoll C., Chen T., Mehta D. & Pernkopf F. (2018) Fixed Points of Belief Propagation - An Analysis via Polynomial Homotopy Continuation. in IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(9), p. 2124-2136. [more info] [doi]
- Conference paper Knoll C. & Pernkopf F. (2017) On Loopy Belief Propagation – Local Stability Analysis for Non-Vanishing Fields.. [more info]
- Conference paper Knoll C., Pernkopf F., Mehta D. & Chen T. (2016) Fixed Points Solutions of Belief Propagation.. [more info]
- Poster Knoll C., Pernkopf F., Mehta D. & Chen T. (2016) Fixed Point Solutions of Belief Propagation.. [more info]
- Conference paper Knoll C., Rath M., Tschiatschek S. & Pernkopf F. (2015) Message Scheduling Methods for Belief Propagation. in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 (pp. 295). [more info] [doi]
- Diploma Thesis Knoll C. (2014) Alternative Descriptions for Random Variables.. [more info]