| 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 | 416 | 2018 |
| Safe exploration in continuous action spaces G Dalal, K Dvijotham, M Vecerik, T Hester, C Paduraru, Y Tassa arXiv preprint arXiv:1801.08757, 2018 | 400 | 2018 |
| 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 | 282 | 2019 |
| Real-time optimal power flow Y Tang, K Dvijotham, S Low IEEE Transactions on Smart Grid 8 (6), 2963-2973, 2017 | 218 | 2017 |
| Inverse optimal control with linearly-solvable MDPs K Dvijotham, E Todorov Proceedings of the 27th International conference on machine learning (ICML …, 2010 | 193 | 2010 |
| Training verified learners with learned verifiers K Dvijotham, S Gowal, R Stanforth, R Arandjelovic, B O'Donoghue, ... arXiv preprint arXiv:1805.10265, 2018 | 169 | 2018 |
| 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 | 159 | 2019 |
| 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 |
| 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 | 143 | 2021 |
| 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, 5318-5331, 2020 | 87 | 2020 |
| Opportunities for price manipulation by aggregators in electricity markets NA Ruhi, N Chen, K Dvijotham, A Wierman ACM SIGMETRICS Performance Evaluation Review 44 (2), 49-51, 2016 | 73 | 2016 |
| Rigorous agent evaluation: An adversarial approach to uncover catastrophic failures J Uesato, A Kumar, C Szepesvari, T Erez, A Ruderman, K Anderson, ... arXiv preprint arXiv:1812.01647, 2018 | 71 | 2018 |
| Error bounds on the DC power flow approximation: A convex relaxation approach K Dvijotham, DK Molzahn 2016 IEEE 55th Conference on Decision and Control (CDC), 2411-2418, 2016 | 67 | 2016 |
| 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 |
| A unified theory of linearly solvable optimal control K Dvijotham, E Todorov Artificial Intelligence (UAI) 1, 2011 | 56* | 2011 |
| Lagrangian decomposition for neural network verification R Bunel, A De Palma, A Desmaison, K Dvijotham, P Kohli, P Torr, ... Conference on Uncertainty in Artificial Intelligence, 370-379, 2020 | 51 | 2020 |
| Constructing convex inner approximations of steady-state security regions HD Nguyen, K Dvijotham, K Turitsyn IEEE Transactions on Power Systems 34 (1), 257-267, 2018 | 49 | 2018 |
| Convex restriction of power flow feasibility sets D Lee, HD Nguyen, K Dvijotham, K Turitsyn IEEE Transactions on Control of Network Systems 6 (3), 1235-1245, 2019 | 48 | 2019 |
| Linearly solvable optimal control K Dvijotham, E Todorov Reinforcement learning and approximate dynamic programming for feedback …, 2012 | 48 | 2012 |
Pushmeet KohliDeepMindVerified email at google.com
Pushmeet KohliDeepMindVerified email at google.com