| Deep reinforcement learning in large discrete action spaces G Dulac-Arnold, R Evans, H van Hasselt, P Sunehag, T Lillicrap, J Hunt, ... arXiv preprint arXiv:1512.07679, 2015 | 625 | 2015 |
| 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 | 428 | 2018 |
| A Dual Approach to Scalable Verification of Deep Networks. K Dvijotham, R Stanforth, S Gowal, TA Mann, P Kohli UAI 1 (2), 3, 2018 | 415 | 2018 |
| 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 | 253 | 2020 |
| 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 | 191 | 2021 |
| Improving robustness using generated data S Gowal, SA Rebuffi, O Wiles, F Stimberg, DA Calian, TA Mann Advances in Neural Information Processing Systems 34, 4218-4233, 2021 | 166 | 2021 |
| Data augmentation can improve robustness SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, TA Mann Advances in Neural Information Processing Systems 34, 29935-29948, 2021 | 156 | 2021 |
| Scalable verified training for provably robust image classification S Gowal, KD Dvijotham, R Stanforth, R Bunel, C Qin, J Uesato, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 145 | 2019 |
| Robust reinforcement learning for continuous control with model misspecification DJ Mankowitz, N Levine, R Jeong, Y Shi, J Kay, A Abdolmaleki, ... arXiv preprint arXiv:1906.07516, 2019 | 99 | 2019 |
| 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 | 67 | 2019 |
| Adaptive skills adaptive partitions (ASAP) DJ Mankowitz, TA Mann, S Mannor Advances in neural information processing systems 29, 2016 | 66 | 2016 |
| Approximate Value Iteration with Temporally Extended Actions SMDP Timothy A. Mann Journal of Artificial Intelligence Research 53, 375-438, 2015 | 62 | 2015 |
| Scaling up approximate value iteration with options: Better policies with fewer iterations T Mann, S Mannor International conference on machine learning, 127-135, 2014 | 60 | 2014 |
| How hard is my MDP?" The distribution-norm to the rescue" OA Maillard, TA Mann, S Mannor Advances in Neural Information Processing Systems 27, 2014 | 57 | 2014 |
| 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 | 56 | 2020 |
| Soft-robust actor-critic policy-gradient E Derman, DJ Mankowitz, TA Mann, S Mannor arXiv preprint arXiv:1803.04848, 2018 | 53 | 2018 |
| Learning robust options D Mankowitz, T Mann, PL Bacon, D Precup, S Mannor Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 45 | 2018 |
| A bayesian approach to robust reinforcement learning E Derman, D Mankowitz, T Mann, S Mannor Uncertainty in Artificial Intelligence, 648-658, 2020 | 44 | 2020 |
| Time regularized interrupting options DJ Mankowitz, TA Mann, S Mannor Internation Conference on Machine Learning, 2014 | 41* | 2014 |
| Defending against image corruptions through adversarial augmentations DA Calian, F Stimberg, O Wiles, SA Rebuffi, A Gyorgy, T Mann, S Gowal arXiv preprint arXiv:2104.01086, 2021 | 38 | 2021 |
Shie MannorProfessor of Electrical Engineering @ Technion & Researcher @ Nvidia ResearchПотвърден имейл адрес: technion.ac.il
