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

Krishnamurthy Dvijotham

Research Scientist, DeepMind

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	224	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	173	2018
Inverse optimal control with linearly-solvable MDPs K Dvijotham, E Todorov ICML, 2010	150	2010
Real-time optimal power flow Y Tang, K Dvijotham, S Low IEEE Transactions on Smart Grid 8 (6), 2963-2973, 2017	120	2017
Safe exploration in continuous action spaces G Dalal, K Dvijotham, M Vecerik, T Hester, C Paduraru, Y Tassa arXiv preprint arXiv:1801.08757, 2018	108	2018
Training verified learners with learned verifiers K Dvijotham, S Gowal, R Stanforth, R Arandjelovic, B O'Donoghue, ... arXiv preprint arXiv:1805.10265, 2018	100	2018
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	96	2019
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	53	2019
A uniﬁed theory of linearly solvable optimal control K Dvijotham, E Todorov Uncertainty in Artificial Intelligence, 2012	50*	2012
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	40	2010
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	39	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	35	2017
Linearly solvable optimal control K Dvijotham, E Todorov Reinforcement learning and approximate dynamic programming for feedback …, 2013	34	2013
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	33	2014
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	33	2011
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	32	2019
Convexity of energy-like functions: Theoretical results and applications to power system operations K Dvijotham, S Low, M Chertkov arXiv preprint arXiv:1501.04052, 2015	26	2015
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	25	2019
Graphical models for optimal power flow K Dvijotham, M Chertkov, P Van Hentenryck, M Vuffray, S Misra Constraints 22 (1), 24-49, 2017	25	2017
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	24	2019

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