Andrey Lokhov
Andrey Lokhov
Los Alamos National Laboratory, Theoretical Division
Verified email at - Homepage
Cited by
Cited by
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
Quantum algorithm implementations for beginners
A Adedoyin, J Ambrosiano, P Anisimov, W Casper, G Chennupati, ...
arXiv preprint arXiv:1804.03719, 2018
Quantum algorithm implementations for beginners
PJ Coles, S Eidenbenz, S Pakin, A Adedoyin, J Ambrosiano, P Anisimov, ...
arXiv, arXiv: 1804.03719, 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 29, 2016
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
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
Optimal structure and parameter learning of Ising models
AY Lokhov, M Vuffray, S Misra, M Chertkov
Science advances 4 (3), e1700791, 2018
Dynamic message-passing equations for models with unidirectional dynamics
AY Lokhov, M Mézard, L Zdeborová
Physical Review E 91 (1), 012811, 2015
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
Reconstructing parameters of spreading models from partial observations
AY Lokhov
Advances in Neural Information Processing Systems, 3467–3475, 2016
Efficient Learning of Discrete Graphical Models
M Vuffray, S Misra, A Lokhov
Advances in Neural Information Processing Systems 33, 13575-13585, 2020
Real-time anomaly detection and classification in streaming PMU data
C Hannon, D Deka, D Jin, M Vuffray, AY Lokhov
2021 IEEE Madrid PowerTech, 1-6, 2021
Information theoretic optimal learning of gaussian graphical models
S Misra, M Vuffray, AY Lokhov
Conference on Learning Theory, 2888-2909, 2020
On the emerging potential of quantum annealing hardware for combinatorial optimization
B Tasseff, T Albash, Z Morrell, M Vuffray, AY Lokhov, S Misra, C Coffrin
arXiv preprint arXiv:2210.04291, 2022
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
Competition, collaboration, and optimization in multiple interacting spreading processes
H Sun, D Saad, AY Lokhov
Physical Review X 11 (1), 011048, 2021
High-quality thermal Gibbs sampling with quantum annealing hardware
J Nelson, M Vuffray, AY Lokhov, T Albash, C Coffrin
Physical Review Applied 17 (4), 044046, 2022
Programmable quantum annealers as noisy gibbs samplers
M Vuffray, C Coffrin, YA Kharkov, AY Lokhov
PRX Quantum 3 (2), 020317, 2022
The potential of quantum annealing for rapid solution structure identification
Y Pang, C Coffrin, AY Lokhov, M Vuffray
Constraints 26 (1), 1-25, 2021
Prediction-centric learning of independent cascade dynamics from partial observations
M Wilinski, A Lokhov
International Conference on Machine Learning, 11182-11192, 2021
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