| Variability in the analysis of a single neuroimaging dataset by many teams R Botvinik-Nezer, F Holzmeister, CF Camerer, A Dreber, J Huber, ... Nature 582 (7810), 84-88, 2020 | 921 | 2020 |
| Learning structural node embeddings via diffusion wavelets C Donnat, M Zitnik, D Hallac, J Leskovec Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 415 | 2018 |
| Tracking network dynamics: A survey using graph distances C Donnat, S Holmes The Annals of Applied Statistics 12 (2), 971-1012, 2018 | 104 | 2018 |
| Geomstats: a Python package for Riemannian geometry in machine learning N Miolane, N Guigui, A Le Brigant, J Mathe, B Hou, Y Thanwerdas, ... Journal of Machine Learning Research 21 (223), 1-9, 2020 | 90 | 2020 |
| Tracking network dynamics: A survey of distances and similarity metrics C Donnat, S Holmes arXiv preprint arXiv:1801.07351, 2018 | 40 | 2018 |
| Modeling the heterogeneity in COVID-19's reproductive number and its impact on predictive scenarios C Donnat, S Holmes Journal of Applied Statistics 50 (11-12), 2518-2546, 2023 | 39 | 2023 |
| Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy C Donnat, A Levy, F Poitevin, N Miolane arXiv preprint arXiv:2201.02867, 2022 | 30 | 2022 |
| Spectral graph wavelets for structural role similarity in networks C Donnat, M Zitnik, D Hallac, J Leskovec | 27 | 2018 |
| A divide-and-conquer framework for large-scale subspace clustering C You, C Donnat, DP Robinson, R Vidal 2016 50th Asilomar Conference on Signals, Systems and Computers, 1014-1018, 2016 | 15 | 2016 |
| Introduction to geometric learning in python with geomstats N Miolane, N Guigui, H Zaatiti, C Shewmake, H Hajri, D Brooks, ... SciPy 2020-19th Python in Science Conference, 48-57, 2020 | 13 | 2020 |
| Statistical modeling for practical pooled testing during the COVID-19 pandemic S Comess, H Wang, S Holmes, C Donnat Statistical Science 37 (2), 229-250, 2022 | 12 | 2022 |
| A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses C Donnat, N Miolane, F Bunbury, J Kreindler Machine Learning for Health, 53-84, 2020 | 8 | 2020 |
| Similarities in extracorporeal membrane oxygenation management across intensive care unit types in the United States: An analysis of the Extracorporeal Life Support … CG Owyang, C Donnat, D Brodie, HB Gershengorn, M Hua, N Qadir, ... Artificial organs 46 (7), 1369-1381, 2022 | 7 | 2022 |
| Predicting COVID-19 transmission to inform the management of mass events: model-based approach C Donnat, F Bunbury, J Kreindler, D Liu, FT Filippidis, T Esko, A El-Osta, ... JMIR Public Health and Surveillance 7 (12), e30648, 2021 | 5 | 2021 |
| Iclr 2022 challenge for computational geometry & topology: Design and results A Myers, S Utpala, S Talbar, S Sanborn, C Shewmake, C Donnat, J Mathe, ... Topological, Algebraic and Geometric Learning Workshops 2022, 269-276, 2022 | 4 | 2022 |
| Convex hierarchical clustering for graph-structured data C Donnat, S Holmes 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1999-2006, 2019 | 3 | 2019 |
| The Generalized Elastic Net for least squares regression with network-aligned signal and correlated design H Tran, S Wei, C Donnat arXiv preprint arXiv:2211.00292, 2022 | 2 | 2022 |
| A Predictive Modelling Framework for COVID-19 Transmission to Inform the Management of Mass Events C Donnat, F Bunbury, J Kreindler, FT Filippidis, A El-Osta, T Esko, ... MedRxiv, 2021.05. 13.21256857, 2021 | 2 | 2021 |
| Variability in the analysis of a single neuroimaging dataset by many teams (Preprint) R Botvinik-Nezer, F Holzmeister, CF Camerer, A Dreber, J Huber, ... | 2 | 2020 |
| Studying the Effect of GNN Spatial Convolutions On The Embedding Space’s Geometry C Donnat, SW Jeong Uncertainty in Artificial Intelligence, 539-548, 2023 | 1 | 2023 |
Susan P HolmesProfessor of Statistics, Stanford University在 stat.stanford.edu 的电子邮件经过验证
