| PRIMA: general and precise neural network certification via scalable convex hull approximations MN Müller, G Makarchuk, G Singh, M Püschel, M Vechev Proceedings of the ACM on Programming Languages 6 (POPL), 1-33, 2022 | 128* | 2022 |
| Complete verification via multi-neuron relaxation guided branch-and-bound C Ferrari, MN Muller, N Jovanovic, M Vechev The Tenth International Conference on Learning Representations, 2022 (ICLR'22), 2022 | 87 | 2022 |
| Certified training: Small boxes are all you need MN Müller, F Eckert, M Fischer, M Vechev The Eleventh International Conference on Learning Representations (ICLR'23), 2022 | 59 | 2022 |
| First three years of the international verification of neural networks competition (VNN-COMP) C Brix, MN Müller, S Bak, TT Johnson, C Liu International Journal on Software Tools for Technology Transfer 25 (3), 329-339, 2023 | 58 | 2023 |
| The third international verification of neural networks competition (VNN-COMP 2022): Summary and results MN Müller, C Brix, S Bak, C Liu, TT Johnson arXiv preprint arXiv:2212.10376, 2022 | 56 | 2022 |
| Boosting randomized smoothing with variance reduced classifiers MZ Horváth, MN Müller, M Fischer, M Vechev The Tenth International Conference on Learning Representations, 2022 (ICLR'22), 2021 | 43 | 2021 |
| Taps: Connecting certified and adversarial training Y Mao, MN Müller, M Fischer, M Vechev Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23), 2023 | 14* | 2023 |
| Certify or predict: Boosting certified robustness with compositional architectures MN Müller, M Balunović, M Vechev The Ninth International Conference on Learning Representations, 2021 (ICLR'21), 2021 | 14 | 2021 |
| Robust and Accurate--Compositional Architectures for Randomized Smoothing MZ Horváth, MN Müller, M Fischer, M Vechev arXiv preprint arXiv:2204.00487, 2022 | 12 | 2022 |
| Abstract interpretation of fixpoint iterators with applications to neural networks MN Müller, M Fischer, R Staab, M Vechev Proceedings of the ACM on Programming Languages 7 (PLDI), 786-810, 2023 | 10* | 2023 |
| 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 | 8 | 2024 |
| Understanding certified training with interval bound propagation Y Mao, MN Müller, M Fischer, M Vechev arXiv preprint arXiv:2306.10426, 2023 | 6 | 2023 |
| Expressivity of ReLU-Networks under Convex Relaxations M Baader, MN Müller, Y Mao, M Vechev arXiv preprint arXiv:2311.04015, 2023 | 4 | 2023 |
| Automated Classification of Model Errors on ImageNet M Peychev, MN Mueller, M Fischer, M Vechev Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23), 2023 | 3 | 2023 |
| Efficient Certified Training and Robustness Verification of Neural ODEs M Zeqiri, MN Müller, M Fischer, M Vechev The Eleventh International Conference on Learning Representations (ICLR'23), 2023 | 3* | 2023 |
| Spear: Exact gradient inversion of batches in federated learning DI Dimitrov, M Baader, MN Müller, M Vechev arXiv preprint arXiv:2403.03945, 2024 | 2 | 2024 |
| Certified Robustness to Data Poisoning in Gradient-Based Training P Sosnin, MN Müller, M Baader, C Tsay, M Wicker arXiv preprint arXiv:2406.05670, 2024 | 1 | 2024 |
| Overcoming the Paradox of Certified Training with Gaussian Smoothing S Balauca, MN Müller, Y Mao, M Baader, M Fischer, M Vechev arXiv preprint arXiv:2403.07095, 2024 | 1 | 2024 |
| Prompt Sketching for Large Language Models L Beurer-Kellner, MN Müller, M Fischer, M Vechev arXiv preprint arXiv:2311.04954, 2023 | 1 | 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 |
Martin VechevFull Professor of Computer Science, ETH Zurich; Scientific Director, INSAIT;在 inf.ethz.ch 的电子邮件经过验证
