| Competition-level code generation with alphacode Y Li, D Choi, J Chung, N Kushman, J Schrittwieser, R Leblond, T Eccles, ... Science 378 (6624), 1092-1097, 2022 | 874 | 2022 |
| On the effectiveness of interval bound propagation for training verifiably robust models S Gowal, K Dvijotham, R Stanforth, R Bunel, C Qin, J Uesato, ... arXiv preprint arXiv:1810.12715, 2018 | 696* | 2018 |
| Fixing data augmentation to improve adversarial robustness SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, T Mann arXiv preprint arXiv:2103.01946, 2021 | 672* | 2021 |
| Challenges of real-world reinforcement learning: definitions, benchmarks and analysis G Dulac-Arnold, N Levine, DJ Mankowitz, J Li, C Paduraru, S Gowal, ... Machine Learning 110 (9), 2419-2468, 2021 | 589* | 2021 |
| A Dual Approach to Scalable Verification of Deep Networks. K Dvijotham, R Stanforth, S Gowal, TA Mann, P Kohli UAI 1 (2), 3, 2018 | 464 | 2018 |
| Towards stable and efficient training of verifiably robust neural networks H Zhang, H Chen, C Xiao, S Gowal, R Stanforth, B Li, D Boning, CJ Hsieh arXiv preprint arXiv:1906.06316, 2019 | 366 | 2019 |
| Adversarial robustness through local linearization C Qin, J Martens, S Gowal, D Krishnan, K Dvijotham, A Fawzi, S De, ... Advances in neural information processing systems 32, 2019 | 335 | 2019 |
| Uncovering the limits of adversarial training against norm-bounded adversarial examples S Gowal, C Qin, J Uesato, T Mann, P Kohli arXiv preprint arXiv:2010.03593, 2020 | 330 | 2020 |
| A fine-grained analysis on distribution shift O Wiles, S Gowal, F Stimberg, S Alvise-Rebuffi, I Ktena, K Dvijotham, ... arXiv preprint arXiv:2110.11328, 2021 | 221 | 2021 |
| Achieving verified robustness to symbol substitutions via interval bound propagation PS Huang, R Stanforth, J Welbl, C Dyer, D Yogatama, S Gowal, ... arXiv preprint arXiv:1909.01492, 2019 | 181 | 2019 |
| Training verified learners with learned verifiers K Dvijotham, S Gowal, R Stanforth, R Arandjelovic, B O'Donoghue, ... arXiv preprint arXiv:1805.10265, 2018 | 181 | 2018 |
| An alternative surrogate loss for pgd-based adversarial testing S Gowal, J Uesato, C Qin, PS Huang, T Mann, P Kohli arXiv preprint arXiv:1910.09338, 2019 | 83 | 2019 |
| Towards robust image classification using sequential attention models D Zoran, M Chrzanowski, PS Huang, S Gowal, A Mott, P Kohli Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 78 | 2020 |
| Evaluating the adversarial robustness of adaptive test-time defenses F Croce, S Gowal, T Brunner, E Shelhamer, M Hein, T Cemgil International Conference on Machine Learning, 4421-4435, 2022 | 67 | 2022 |
| The autoencoding variational autoencoder T Cemgil, S Ghaisas, K Dvijotham, S Gowal, P Kohli Advances in Neural Information Processing Systems 33, 15077-15087, 2020 | 60 | 2020 |
| Achieving robustness in the wild via adversarial mixing with disentangled representations S Gowal, C Qin, PS Huang, T Cemgil, K Dvijotham, T Mann, P Kohli Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 60 | 2020 |
| A framework for robustness certification of smoothed classifiers using f-divergences KD Dvijotham, J Hayes, B Balle, Z Kolter, C Qin, A Gyorgy, K Xiao, ... International Conference on Learning Representations, 2020 | 57 | 2020 |
| Differentially private diffusion models generate useful synthetic images S Ghalebikesabi, L Berrada, S Gowal, I Ktena, R Stanforth, J Hayes, S De, ... arXiv preprint arXiv:2302.13861, 2023 | 52 | 2023 |
| Beyond greedy ranking: Slate optimization via list-CVAE R Jiang, S Gowal, TA Mann, DJ Rezende arXiv preprint arXiv:1803.01682, 2018 | 50 | 2018 |
| Verification of non-linear specifications for neural networks C Qin, B O'Donoghue, R Bunel, R Stanforth, S Gowal, J Uesato, ... arXiv preprint arXiv:1902.09592, 2019 | 48 | 2019 |
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