| Adversarial risk and the dangers of evaluating against weak attacks J Uesato, B O'Donoghue, A Oord, P Kohli ICML 2018, 2018 | 236 | 2018 |
| Robustfill: Neural program learning under noisy I/O J Devlin, J Uesato, S Bhupatiraju, R Singh, A Mohamed, P Kohli Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 215 | 2017 |
| 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 | 139 | 2018 |
| Technical report on the cleverhans v2. 1.0 adversarial examples library N Papernot, F Faghri, N Carlini, I Goodfellow, R Feinman, A Kurakin, ... arXiv preprint arXiv:1610.00768, 2016 | 132 | 2016 |
| Are Labels Required for Improving Adversarial Robustness? J Uesato, JB Alayrac, PS Huang, R Stanforth, A Fawzi, P Kohli NeurIPS 2019, 2019 | 88* | 2019 |
| Robustness via curvature regularization, and vice versa SM Moosavi-Dezfooli, A Fawzi, J Uesato, P Frossard Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 84 | 2019 |
| Training verified learners with learned verifiers K Dvijotham, S Gowal, R Stanforth, R Arandjelovic, B O'Donoghue, ... arXiv preprint arXiv:1805.10265, 2018 | 82 | 2018 |
| Rigorous agent evaluation: An adversarial approach to uncover catastrophic failures J Uesato, A Kumar, C Szepesvari, T Erez, A Ruderman, K Anderson, ... ICLR 2019, 2018 | 24 | 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 | 22 | 2019 |
| Verification of non-linear specifications for neural networks C Qin, B O'Donoghue, R Bunel, R Stanforth, S Gowal, J Uesato, ... ICLR 2019, 2019 | 22 | 2019 |
| Semantic code repair using neuro-symbolic transformation networks J Devlin, J Uesato, R Singh, P Kohli arXiv preprint arXiv:1710.11054, 2017 | 17 | 2017 |
| Scalable Verified Training for Provably Robust Image Classification S Gowal, KD Dvijotham, R Stanforth, R Bunel, C Qin, J Uesato, ... Proceedings of the IEEE International Conference on Computer Vision, 4842-4851, 2019 | 10 | 2019 |
| Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis A Ruderman, R Everett, B Sikder, H Soyer, J Uesato, A Kumar, C Beattie, ... | 5 | 2018 |
| Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming S Dathathri, K Dvijotham, A Kurakin, A Raghunathan, J Uesato, RR Bunel, ... Advances in Neural Information Processing Systems 33, 2020 | 1 | 2020 |
| Avoiding Tampering Incentives in Deep RL via Decoupled Approval J Uesato, R Kumar, V Krakovna, T Everitt, R Ngo, S Legg arXiv preprint arXiv:2011.08827, 2020 | | 2020 |
| REALab: An Embedded Perspective on Tampering R Kumar, J Uesato, R Ngo, T Everitt, V Krakovna, S Legg arXiv preprint arXiv:2011.08820, 2020 | | 2020 |
| 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 | | 2020 |
| Systems and methods for entering traffic flow in autonomous vehicles J Allan, E Lujan, P Gao, S Bhattacharya, W Mou, J Uesato US Patent 10,488,861, 2019 | | 2019 |
