| Robustness of graph neural networks at scale S Geisler, T Schmidt, H Şirin, D Zügner, A Bojchevski, S Günnemann Advances in Neural Information Processing Systems 34, 7637-7649, 2021 | 134 | 2021 |
| Reliable Graph Neural Networks via Robust Aggregation S Geisler, D Zügner, S Günnemann Advances in Neural Information Processing Systems 33, 2020 | 88 | 2020 |
| 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 | 79 | 2021 |
| 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 (ICLR), 2022 | 72* | 2022 |
| Are Defenses for Graph Neural Networks Robust? S Geisler*, F Mujkanovic*, S Günnemann, A Bojchevski Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022 | 66* | 2022 |
| Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness S Geisler, J Sommer, J Schuchardt, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2022 | 42 | 2022 |
| Transformers Meet Directed Graphs S Geisler, Y Li, D Mankowitz, AT Cemgil, S Günnemann, C Paduraru International Conference on Machine Learning (ICML), 2023 | 34 | 2023 |
| Attacking Large Language Models with Projected Gradient Descent S Geisler, T Wollschläger, MHI Abdalla, J Gasteiger, S Günnemann arXiv preprint arXiv:2402.09154, 2024 | 32 | 2024 |
| 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 | 29 | 2022 |
| Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions S Geisler*, L Gosch*, D Sturm*, B Charpentier, D Zügner, S Günnemann Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 22* | 2023 |
| Revisiting Robustness in Graph Machine Learning L Gosch, D Sturm, S Geisler, S Günnemann International Conference on Learning Representations (ICLR), 2023 | 22 | 2023 |
| Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks Y Scholten, J Schuchardt, S Geisler, A Bojchevski, S Günnemann Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022 | 19 | 2022 |
| Time-series features for predictive policing J Borges, D Ziehr, M Beigl, N Cacho, A Martins, A Araujo, L Bezerra, ... 2018 IEEE international smart cities conference (ISC2), 1-8, 2018 | 16 | 2018 |
| Method and control and detection unit for checking the plausibility of a wrong-way driving incident of a motor vehicle C Jeschke, C Braeuchle, S Geisler US Patent 9,786,166, 2017 | 12 | 2017 |
| Method, device and system for wrong-way driver detection S Geisler US Patent 10,916,124, 2021 | 7 | 2021 |
| Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning J Feng, J Lee, S Geisler, S Günnemann, R Triebel 7th Annual Conference on Robot Learning, 2023 | 6 | 2023 |
| Attacking Graph Neural Networks at Scale S Geisler, D Zügner, A Bojchevski, S Günnemann DLG workshop @ AAAI, 2021 | 6 | 2021 |
| On the Robustness and Anomaly Detection of Sparse Neural Networks M Ayle, B Charpentier, J Rachwan, D Zügner, S Geisler, S Günnemann arXiv preprint arXiv:2207.04227, 2022 | 5 | 2022 |
| Method for determining a coefficient of friction for a contact between a tire of a vehicle and a roadway, and method for controlling a vehicle function of a vehicle C Lellmann, S Geisler US Patent App. 16/100,390, 2019 | 5 | 2019 |
| Method and system for warning a driver of a vehicle S Geisler, C Jeschke US Patent App. 15/207,987, 2017 | 3 | 2017 |
Stephan GünnemannProfessor of Computer Science, Technical University of MunichVerified email at in.tum.de
