| Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts B Charpentier, D Zügner, S Günnemann Advances in Neural Information Processing Systems 33, 2020 | 84 | 2020 |
| Hierarchical graph clustering using node pair sampling T Bonald, B Charpentier, A Galland, A Hollocou arXiv preprint arXiv:1806.01664, 2018 | 48 | 2018 |
| Scikit-network: Graph analysis in python T Bonald, N De Lara, Q Lutz, B Charpentier The Journal of Machine Learning Research 21 (1), 7543-7548, 2020 | 38 | 2020 |
| Evaluating robustness of predictive uncertainty estimation: Are Dirichlet-based models reliable? AK Kopetzki, B Charpentier, D Zügner, S Giri, S Günnemann International Conference on Machine Learning, 5707-5718, 2021 | 32 | 2021 |
| Graph posterior network: Bayesian predictive uncertainty for node classification M Stadler, B Charpentier, S Geisler, D Zügner, S Günnemann Advances in Neural Information Processing Systems 34, 18033-18048, 2021 | 29 | 2021 |
| Uncertainty on asynchronous time event prediction M Biloš, B Charpentier, S Günnemann Advances in Neural Information Processing Systems 32, 2019 | 29 | 2019 |
| Natural posterior network: Deep bayesian predictive uncertainty for exponential family distributions B Charpentier, O Borchert, D Zügner, S Geisler, S Günnemann International Conference on Learning Representations, 2021 | 22* | 2021 |
| Differentiable DAG Sampling B Charpentier, S Kibler, S Günnemann International Conference on Learning Representations, 2022 | 12 | 2022 |
| Winning the lottery ahead of time: Efficient early network pruning J Rachwan, D Zügner, B Charpentier, S Geisler, M Ayle, S Günnemann International Conference on Machine Learning, 18293-18309, 2022 | 8 | 2022 |
| Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning B Charpentier, R Senanayake, M Kochenderfer, S Günnemann Distribution-Free Uncertainty Quantification Workshop (DFUQ - ICML), 2022 | 6 | 2022 |
| On the Robustness and Anomaly Detection of Sparse Neural Networks M Ayle, B Charpentier, J Rachwan, D Zügner, S Geisler, S Günnemann Sparsity in Neural Networks Workshop (SNN), 2022 | 3 | 2022 |
| Tree sampling divergence: an information-theoretic metric for hierarchical graph clustering B Charpentier, T Bonald IJCAI-19, 2019 | 3 | 2019 |
| Multi-scale clustering in graphs using modularity B Charpentier | 3 | 2019 |
| Edge Directionality Improves Learning on Heterophilic Graphs E Rossi, B Charpentier, F Di Giovanni, F Frasca, S Günnemann, ... arXiv preprint arXiv:2305.10498, 2023 | 2 | 2023 |
| End-to-end learning of probabilistic hierarchies on graphs D Zügner, B Charpentier, M Ayle, S Geringer, S Günnemann International Conference on Learning Representations, 2021 | 2 | 2021 |
| Uncertainty Estimation for Molecules: Desiderata and Methods T Wollschläger, N Gao, B Charpentier, MA Ketata, S Günnemann | 1 | 2023 |
| Learning Graph Representations by Dendrograms T Bonald, B Charpentier arXiv preprint arXiv:1807.05087, 2018 | 1 | 2018 |
| Adversarial Training for Graph Neural Networks L Gosch, S Geisler, D Sturm, B Charpentier, D Zügner, S Günnemann arXiv preprint arXiv:2306.15427, 2023 | | 2023 |
| Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models J Getzner, B Charpentier, S Günnemann Tackling Climate Change with Machine Learning: Global Perspectives and Local …, 2023 | | 2023 |
| Training, Architecture, and Prior for Deterministic Uncertainty Methods B Charpentier, C Zhang, S Günnemann Pitfalls of limited data and computation for Trustworthy ML Workshop …, 2023 | | 2023 |
Stephan GünnemannProfessor of Computer Science, Technical University of Munich在 in.tum.de 的电子邮件经过验证
