| Dynotears: Structure learning from time-series data R Pamfil, N Sriwattanaworachai, S Desai, P Pilgerstorfer, K Georgatzis, ... International Conference on Artificial Intelligence and Statistics, 1595-1605, 2020 | 145 | 2020 |
| Relating modularity maximization and stochastic block models in multilayer networks AR Pamfil, SD Howison, R Lambiotte, MA Porter SIAM Journal on Mathematics of Data Science 1 (4), 667-698, 2019 | 42 | 2019 |
| Methods of proof in random matrix theory AR Feier Harvard University, 2012 | 28 | 2012 |
| Global air transport complex network: multi-scale analysis W Guo, B Toader, R Feier, G Mosquera, F Ying, SW Oh, M Price-Williams, ... SN Applied Sciences 1, 1-14, 2019 | 21 | 2019 |
| Inference of edge correlations in multilayer networks AR Pamfil, SD Howison, MA Porter Physical Review E 102 (6), 062307, 2020 | 14* | 2020 |
| Communities in annotated, multilayer, and correlated networks A Pamfil University of Oxford, 2018 | 6 | 2018 |
| Represent the Degree of Mimicry between Prosodic Behaviour of Speech Between Two or More People M Arran, R Assier, G Benham, B Deka, L Dempsey, E Dubrovina, N Fadai, ... | | 2015 |
| EPSRC Centre for Doctoral Training in Industrially Focused Mathematical Modelling AR Pamfil | | |
| SUPPLEMENTARY MATERIALS: Relating Modularity Maximization and Stochastic Block Models in Multilayer Networks AR Pamfil, SD Howison, R Lambiotte, MA Porter | | |
| Supplementary Materials for DYNOTEARS: Structure Learning from Time-Series Data R Pamfil, N Sriwattanaworachai, S Desai11, P Pilgerstorfer, P Beaumont, ... | | |
