| Active bayesian causal inference C Toth, L Lorch, C Knoll, A Krause, F Pernkopf, R Peharz, J Von Kügelgen Advances in Neural Information Processing Systems 35, 16261-16275, 2022 | 35 | 2022 |
| Message scheduling methods for belief propagation C Knoll, M Rath, S Tschiatschek, F Pernkopf Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 25 | 2015 |
| Fixed points of belief propagation—an analysis via polynomial homotopy continuation C Knoll, D Mehta, T Chen, F Pernkopf IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (9), 2124-2136, 2017 | 20 | 2017 |
| On Loopy Belief Propagation–Local Stability Analysis for Non-Vanishing Fields C Knoll, F Pernkopf Uncertainty in Artificial Intelligence, 2017 | 19 | 2017 |
| Belief propagation: Accurate marginals or accurate partition function–where is the difference? C Knoll, F Pernkopf Uncertainty in Artificial Intelligence, 627-636, 2020 | 11 | 2020 |
| Self-Guided Belief Propagation--A Homotopy Continuation Method C Knoll, A Weller, F Pernkopf arXiv preprint arXiv:1812.01339, 2018 | 9 | 2018 |
| Distribution mismatch correction for improved robustness in deep neural networks A Fuchs, C Knoll, F Pernkopf arXiv preprint arXiv:2110.01955, 2021 | 6 | 2021 |
| Fixing the bethe approximation: How structural modifications in a graph improve belief propagation H Leisenberger, F Pernkopf, C Knoll Uncertainty in Artificial Intelligence, 1085-1095, 2022 | 5 | 2022 |
| Convergence behavior of belief propagation: estimating regions of attraction via lyapunov functions H Leisenberger, C Knoll, R Seeber, F Pernkopf Uncertainty in Artificial Intelligence, 1863-1873, 2021 | 4 | 2021 |
| Understanding the Behavior of Belief Propagation: Convergence Properties, Approximation Quality, and Solution Space Analysis C Knoll | 4* | |
| Self-Guided Belief Propagation–A Homotopy Continuation Method C Knoll, A Weller, F Pernkopf IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (4), 5139-5157, 2022 | 2 | 2022 |
| On the Convexity and Reliability of the Bethe Free Energy Approximation H Leisenberger, C Knoll, F Pernkopf arXiv preprint arXiv:2405.15514, 2024 | 1 | 2024 |
| Self-attention for enhanced OAMP detection in MIMO systems A Fuchs, C Knoll, NN Moghadam, A Pak, J Huang, E Leitinger, F Pernkopf ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 1 | 2023 |
| Fixed point solutions of belief propagation C Knoll, F Pernkopf, D Mehta, T Chen NIPS-Workshop: Advances in Approximate Bayesian Inference, 2016 | 1 | 2016 |
| The Muculants–a Higher-Order Statistical Approach C Knoll, BC Geiger, G Kubin arXiv preprint arXiv:1506.04518, 2015 | 1 | 2015 |
| On Loopy Belief Propagation for SASCAs: An Analysis and Empirical Study of the Inference Problem R Nagpal, G Cassiers, C Knoll, F Pernkopf, R Primas, S Mangard IACR Communications in Crypology 1 (4), 15, 2025 | | 2025 |
| Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles S Steger, C Knoll, B Klein, H Fröning, F Pernkopf arXiv preprint arXiv:2412.15758, 2024 | | 2024 |
| Rao-Blackwellising Bayesian Causal Inference C Toth, C Knoll, F Pernkopf, R Peharz arXiv preprint arXiv:2402.14781, 2024 | | 2024 |
| Reliability Thresholds for the Bethe Free Energy Approximation H Leisenberger, C Knoll, F Pernkopf ICML 2024 Workshop on Structured Probabilistic Inference {\&} Generative …, 2024 | | 2024 |
| Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders C Toth, C Knoll, F Pernkopf, R Peharz ICML 2024 Workshop on Structured Probabilistic Inference {\&} Generative …, 2024 | | 2024 |