| Quantum robustness verification: A hybrid quantum-classical neural network certification algorithm N Franco, T Wollschläger, N Gao, JM Lorenz, S Günnemann International Conference on Quantum Computing and Engineering (QCE), 142-153, 2022 | 7 | 2022 |
| Uncertainty Estimation for Molecules: Desiderata and Methods T Wollschläger, N Gao, B Charpentier, MA Ketata, S Günnemann International Conference on Machine Learning (ICML), 2023 | 5 | 2023 |
| Localized Randomized Smoothing for Collective Robustness Certification J Schuchardt*, T Wollschläger*, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2023 | 5 | 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 | 2 | 2024 |
| Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness F Campi, L Gosch, T Wollschläger, Y Scholten, S Günnemann New Frontiers in Adversarial Machine Learning (ADVML FRONTIERS), 2023 | 1 | 2023 |
| Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness N Franco, T Wollschläger, B Poggel, S Günnemann, JM Lorenz International Conference on Quantum Computing and Engineering (QCE), 2023 | 1 | 2023 |
| Uncertainty for Active Learning on Graphs D Fuchsgruber, T Wollschläger, B Charpentier, A Oroz, S Günnemann | | 2023 |
| On Quantum Computing for Neural Network Robustness Verification N Franco, T Wollschläger, N Gao, JM Lorenz, S Günnemann Workshop on Formal Verification of Machine Learning, 2022 | | 2022 |
