Andrey Lokhov
Andrey Lokhov
Los Alamos National Laboratory, Theoretical Division
Verified email at lanl.gov - Homepage
Title
Cited by
Cited by
Year
Inferring the origin of an epidemic with a dynamic message-passing algorithm
AY Lokhov, M Mézard, H Ohta, L Zdeborová
Physical Review E 90 (1), 012801, 2014
2182014
Quantum Algorithm Implementations for Beginners
J Abhijith, A Adedoyin, J Ambrosiano, P Anisimov, A Bärtschi, W Casper, ...
arXiv, arXiv: 1804.03719, 2018
87*2018
Interaction screening: Efficient and sample-optimal learning of Ising models
M Vuffray, S Misra, A Lokhov, M Chertkov
Advances in Neural Information Processing Systems, 2595-2603, 2016
632016
Transferable dynamic molecular charge assignment using deep neural networks
B Nebgen, N Lubbers, JS Smith, AE Sifain, A Lokhov, O Isayev, ...
Journal of chemical theory and computation 14 (9), 4687-4698, 2018
532018
Discovering a transferable charge assignment model using machine learning
AE Sifain, N Lubbers, BT Nebgen, JS Smith, AY Lokhov, O Isayev, ...
The journal of physical chemistry letters 9 (16), 4495-4501, 2018
532018
Dynamic message-passing equations for models with unidirectional dynamics
AY Lokhov, M Mézard, L Zdeborová
Physical Review E 91 (1), 012811, 2015
422015
Optimal structure and parameter learning of Ising models
AY Lokhov, M Vuffray, S Misra, M Chertkov
Science advances 4 (3), e1700791, 2018
362018
Optimal deployment of resources for maximizing impact in spreading processes
AY Lokhov, D Saad
Proceedings of the National Academy of Sciences 114 (39), E8138-E8146, 2017
362017
Reconstructing parameters of spreading models from partial observations
A Lokhov
Advances in Neural Information Processing Systems, 3467-3475, 2016
162016
Online learning of power transmission dynamics
AY Lokhov, M Vuffray, D Shemetov, D Deka, M Chertkov
2018 Power Systems Computation Conference (PSCC), 1-7, 2018
132018
Efficient learning of discrete graphical models
M Vuffray, S Misra, AY Lokhov
arXiv preprint arXiv:1902.00600, 2019
102019
Information theoretic optimal learning of gaussian graphical models
S Misra, M Vuffray, AY Lokhov
Conference on Learning Theory, 2888-2909, 2020
9*2020
Detection of cyber-physical faults and intrusions from physical correlations
AY Lokhov, N Lemons, TC McAndrew, A Hagberg, S Backhaus
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW …, 2016
82016
Efficient reconstruction of transmission probabilities in a spreading process from partial observations
AY Lokhov, T Misiakiewicz
arXiv preprint arXiv:1509.06893, 2015
82015
Dynamic cavity method and problems on graphs
AY Lokhov
Paris 11, 2014
62014
The Potential of Quantum Annealing for Rapid Solution Structure Identification
Y Pang, C Coffrin, AY Lokhov, M Vuffray
arXiv preprint arXiv:1912.01759, 2019
42019
Scalable Influence Estimation Without Sampling
AY Lokhov, D Saad
arXiv preprint arXiv:1912.12749, 2019
32019
State and noise covariance estimation in power grids using limited nodal pmus
D Deka, A Zare, A Lokhov, M Jovanovic, M Chertkov
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2017
32017
Phase transition in random planar diagrams and RNA-type matching
AY Lokhov, OV Valba, MV Tamm, SK Nechaev
Physical Review E 88 (5), 052117, 2013
32013
Uncovering Power Transmission Dynamic Model from Incomplete PMU Observations
AY Lokhov, D Deka, M Vuffray, M Chertkov
2018 IEEE Conference on Decision and Control (CDC), 4008-4013, 2018
22018
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