| 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 | 372 | 2014 |
| Quantum algorithm implementations for beginners A Adedoyin, J Ambrosiano, P Anisimov, W Casper, G Chennupati, ... arXiv preprint arXiv:1804.03719, 2018 | 208 | 2018 |
| Quantum algorithm implementations for beginners PJ Coles, S Eidenbenz, S Pakin, A Adedoyin, J Ambrosiano, P Anisimov, ... arXiv, arXiv: 1804.03719, 2018 | 147 | 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 | 143 | 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 | 140 | 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 | 124 | 2018 |
| Optimal structure and parameter learning of Ising models AY Lokhov, M Vuffray, S Misra, M Chertkov Science advances 4 (3), e1700791, 2018 | 108 | 2018 |
| Dynamic message-passing equations for models with unidirectional dynamics AY Lokhov, M Mézard, L Zdeborová Physical Review E 91 (1), 012811, 2015 | 83 | 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 | 78 | 2017 |
| 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 Journal of Heuristics 30 (5), 325-358, 2024 | 53 | 2024 |
| Efficient Learning of Discrete Graphical Models M Vuffray, S Misra, A Lokhov Advances in Neural Information Processing Systems 33, 13575-13585, 2020 | 44 | 2020 |
| Reconstructing parameters of spreading models from partial observations AY Lokhov Advances in Neural Information Processing Systems, 3467–3475, 2016 | 42 | 2016 |
| 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 | 36 | 2021 |
| Information theoretic optimal learning of gaussian graphical models S Misra, M Vuffray, AY Lokhov Conference on Learning Theory, 2888-2909, 2020 | 32* | 2020 |
| 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 | 28 | 2022 |
| Programmable quantum annealers as noisy Gibbs samplers M Vuffray, C Coffrin, YA Kharkov, AY Lokhov PRX Quantum 3 (2), 020317, 2022 | 27 | 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 | 24 | 2018 |
| Competition, collaboration, and optimization in multiple interacting spreading processes H Sun, D Saad, AY Lokhov Physical Review X 11 (1), 011048, 2021 | 23 | 2021 |
| The potential of quantum annealing for rapid solution structure identification Y Pang, C Coffrin, AY Lokhov, M Vuffray Constraints 26 (1), 1-25, 2021 | 22 | 2021 |
| Prediction-centric learning of independent cascade dynamics from partial observations M Wilinski, A Lokhov International Conference on Machine Learning, 11182-11192, 2021 | 20* | 2021 |
Marc VuffrayLos Alamos National LaboratoryVerified email at lanl.gov
