| Towards neural networks that provably know when they don't know A Meinke, M Hein arXiv preprint arXiv:1909.12180, 2019 | 156 | 2019 |
| Adversarial robustness on in-and out-distribution improves explainability M Augustin, A Meinke, M Hein European Conference on Computer Vision, 228-245, 2020 | 87 | 2020 |
| Certifiably adversarially robust detection of out-of-distribution data J Bitterwolf, A Meinke, M Hein Advances in Neural Information Processing Systems 33, 16085-16095, 2020 | 73 | 2020 |
| Breaking down out-of-distribution detection: Many methods based on ood training data estimate a combination of the same core quantities J Bitterwolf, A Meinke, M Augustin, M Hein International Conference on Machine Learning, 2041-2074, 2022 | 24 | 2022 |
| Provably Adversarially Robust Detection of Out-of-distribution Data (almost) for free A Meinke, J Bitterwolf, M Hein Advances in Neural Information Processing Systems 35, 30167-30180, 2022 | 23* | 2022 |
| Network inference and maximum entropy estimation on information diagrams EA Martin, J Hlinka, A Meinke, F Děchtěrenko, J Tintěra, I Oliver, ... Scientific reports 7 (1), 7062, 2017 | 14 | 2017 |
| Towards a situational awareness benchmark for LLMs R Laine, A Meinke, O Evans Socially Responsible Language Modelling Research, 2023 | 3 | 2023 |
| Classifiers should do well even on their worst classes J Bitterwolf, A Meinke, V Boreiko, M Hein ICML 2022 Shift Happens Workshop, 2022 | 3 | 2022 |
| Tell, don't show: Declarative facts influence how LLMs generalize A Meinke, O Evans arXiv preprint arXiv:2312.07779, 2023 | | 2023 |
| Robust Out-of-Distribution Detection in Deep Classifiers A Meinke Universität Tübingen, 2023 | | 2023 |
| Improving Fairness and Cybersecurity in the Artificial Intelligence Act G Carovano, A Meinke | | 2023 |
| Applications of the Extremal Functional Bootstrap A Meinke Universidade de São Paulo, 2018 | | 2018 |
