| Dl2: Training and querying neural networks with logic M Fischer, M Balunovic, D Drachsler-Cohen, T Gehr, C Zhang, M Vechev International Conference on Machine Learning, 1931-1941, 2019 | 168 | 2019 |
| Learning certified individually fair representations A Ruoss, M Balunovic, M Fischer, M Vechev Advances in Neural Information Processing Systems 33, 7584-7596, 2020 | 90 | 2020 |
| Certified Defense to Image Transformations via Randomized Smoothing M Fischer, M Baader, M Vechev Advances in Neural Information Processing Systems 33, 2020 | 64 | 2020 |
| Prompting is programming: A query language for large language models L Beurer-Kellner, M Fischer, M Vechev Proceedings of the ACM on Programming Languages 7 (PLDI), 1946-1969, 2023 | 60 | 2023 |
| Online robustness training for deep reinforcement learning M Fischer, M Mirman, S Stalder, M Vechev arXiv preprint arXiv:1911.00887, 2019 | 47* | 2019 |
| Boosting Randomized Smoothing with Variance Reduced Classifiers MZ Horváth, MN Mueller, M Fischer, M Vechev International Conference on Learning Representations, 2021 | 37 | 2021 |
| Scalable Certified Segmentation via Randomized Smoothing M Fischer, M Baader, M Vechev International Conference on Machine Learning, 3340-3351, 2021 | 37 | 2021 |
| Certified Training: Small Boxes are All You Need MN Müller, F Eckert, M Fischer, M Vechev arXiv preprint arXiv:2210.04871, 2022 | 28 | 2022 |
| Robust relational layout synthesis from examples for Android P Bielik, M Fischer, M Vechev Proceedings of the ACM on Programming Languages 2 (OOPSLA), 1-29, 2018 | 25 | 2018 |
| Connecting Certified and Adversarial Training Y Mao, M Müller, M Fischer, M Vechev Advances in Neural Information Processing Systems 36, 2024 | 10* | 2024 |
| Private and Reliable Neural Network Inference N Jovanovic, M Fischer, S Steffen, M Vechev Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications …, 2022 | 10 | 2022 |
| Robust and Accurate--Compositional Architectures for Randomized Smoothing MZ Horváth, MN Müller, M Fischer, M Vechev arXiv preprint arXiv:2204.00487, 2022 | 10 | 2022 |
| Shared certificates for neural network verification M Fischer, C Sprecher, DI Dimitrov, G Singh, M Vechev International Conference on Computer Aided Verification, 127-148, 2022 | 8 | 2022 |
| Effective Certification of Monotone Deep Equilibrium Models MN Müller, R Staab, M Fischer, M Vechev arXiv preprint arXiv:2110.08260, 2021 | 6 | 2021 |
| Evading Data Contamination Detection for Language Models is (too) Easy J Dekoninck, MN Müller, M Baader, M Fischer, M Vechev arXiv preprint arXiv:2402.02823, 2024 | 3 | 2024 |
| Automated classification of model errors on imagenet M Peychev, M Müller, M Fischer, M Vechev Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Controlled Text Generation via Language Model Arithmetic J Dekoninck, M Fischer, L Beurer-Kellner, M Vechev arXiv preprint arXiv:2311.14479, 2023 | 2 | 2023 |
| Understanding certified training with interval bound propagation Y Mao, MN Müller, M Fischer, M Vechev arXiv preprint arXiv:2306.10426, 2023 | 2 | 2023 |
| (De-) Randomized Smoothing for Decision Stump Ensembles M Horváth, M Müller, M Fischer, M Vechev Advances in Neural Information Processing Systems 35, 3066-3081, 2022 | 1 | 2022 |
| Efficient Robustness Verification of Neural Ordinary Differential Equations M Zeqiri, MN Mueller, M Fischer, M Vechev The Symbiosis of Deep Learning and Differential Equations II, 2022 | 1 | 2022 |
Martin VechevFull Professor of Computer Science, ETH Zurich; Scientific Director, INSAIT;Verified email at inf.ethz.ch
