Krishnamurthy Dvijotham
Krishnamurthy Dvijotham
Google DeepMind
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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
Safe exploration in continuous action spaces
G Dalal, K Dvijotham, M Vecerik, T Hester, C Paduraru, Y Tassa
arXiv preprint arXiv:1801.08757, 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
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
Real-time optimal power flow
Y Tang, K Dvijotham, S Low
IEEE Transactions on Smart Grid 8 (6), 2963-2973, 2017
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
Inverse optimal control with linearly-solvable MDPs
K Dvijotham, E Todorov
Proceedings of the 27th International conference on machine learning (ICML …, 2010
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
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
Training verified learners with learned verifiers
K Dvijotham, S Gowal, R Stanforth, R Arandjelovic, B O'Donoghue, ...
arXiv preprint arXiv:1805.10265, 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, 5318-5331, 2020
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
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
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
Opportunities for price manipulation by aggregators in electricity markets
NA Ruhi, K Dvijotham, N Chen, A Wierman
IEEE Transactions on Smart Grid 9 (6), 5687-5698, 2017
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
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
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
A unified theory of linearly solvable optimal control
K Dvijotham, E Todorov
Artificial Intelligence (UAI) 1, 2011
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
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