Krishnamurthy Dvijotham

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Pushmeet KohliDeepMindVerified email at google.com
Sven GowalDeepMindVerified email at deepmind.com
Emo TodorovAssociate Professor of Applied Mathematics and Computer Science & Engineering, University ofVerified email at cs.washington.edu
Timothy A MannMetaVerified email at fb.com
Michael (Misha) ChertkovChair, Program in Applied Mathematics, University of ArizonaVerified email at math.arizona.edu
Rudy BunelDeepmindVerified email at google.com
Jonathan UesatoDeepMindVerified email at google.com
Hung Nguyen D.Assistant Professor of Electrical and Electronic Engineering, Nanyang Technological UniversityVerified email at ntu.edu.sg
Steven H. LowGilloon Professor of Computing+Mathematical Sciences, Electrical Engineering, CaltechVerified email at caltech.edu
Maryam FazelProfessor of Electrical and Computer Engineering, University of WashingtonVerified email at uw.edu
Relja Arandjelović / Реља Аранђелов...DeepMindVerified email at google.com
Po-Sen HuangResearch Scientist, DeepMindVerified email at google.com
Scott BackhausNational Institute of Standards and TechnologyVerified email at nist.gov
Alhussein FawziResearch Scientist, Google DeepMindVerified email at google.com
Soham DeDeepMindVerified email at google.com
Daniel K. MolzahnAssistant Professor at the Georgia Institute of Technology. Computational Engineer at ArgonneVerified email at gatech.edu
Sidhant MisraScientist, Applied Mathematics Los Alamos National LaboratoryVerified email at lanl.gov
Yujie TangSchool of Engineering and Applied Sciences, Harvard UniversityVerified email at seas.harvard.edu
Brendan O'DonoghueStanford University, DeepMindVerified email at alumni.stanford.edu
Todd HesterApplied Scientist, Amazon ScoutVerified email at amazon.com

Krishnamurthy Dvijotham

Research Scientist, Google Brain

Verified email at cs.washington.edu - Homepage

reinforcement learning deep learning stochastic control optimization power systems


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A Dual Approach to Scalable Verification of Deep Networks. K Dvijotham, R Stanforth, S Gowal, TA Mann, P Kohli UAI 1 (2), 3, 2018	275	2018
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	225	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	181	2018
Inverse optimal control with linearly-solvable MDPs K Dvijotham, E Todorov ICML, 2010	162	2010
Adversarial robustness through local linearization C Qin, J Martens, S Gowal, D Krishnan, K Dvijotham, A Fawzi, S De, ... arXiv preprint arXiv:1907.02610, 2019	147	2019
Real-time optimal power flow Y Tang, K Dvijotham, S Low IEEE Transactions on Smart Grid 8 (6), 2963-2973, 2017	146	2017
Training verified learners with learned verifiers K Dvijotham, S Gowal, R Stanforth, R Arandjelovic, B O'Donoghue, ... arXiv preprint arXiv:1805.10265, 2018	118	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	76	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	58	2019
A uniﬁed theory of linearly solvable optimal control K Dvijotham, E Todorov Uncertainty in Artificial Intelligence, 2012	51*	2012
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	49	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	48	2017
A nullspace analysis of the nuclear norm heuristic for rank minimization K Dvijotham, M Fazel 2010 IEEE International Conference on Acoustics, Speech and Signal …, 2010	41	2010
Storage sizing and placement through operational and uncertainty-aware simulations K Dvijotham, M Chertkov, S Backhaus 2014 47th Hawaii international conference on system sciences, 2408-2416, 2014	37	2014
Linearly solvable optimal control K Dvijotham, E Todorov Reinforcement learning and approximate dynamic programming for feedback …, 2012	37	2012
Operations-based planning for placement and sizing of energy storage in a grid with a high penetration of renewables K Dvijotham, S Backhaus, M Chertkov arXiv preprint arXiv:1107.1382, 2011	37	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	33	2019
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	31	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	30	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	29	2019

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