From distance correlation to multiscale graph correlation C Shen, CE Priebe, JT Vogelstein Journal of the American Statistical Association, 1-22, 2019 | 72 | 2019 |
The chi-square test of distance correlation C Shen, S Panda, JT Vogelstein Journal of Computational and Graphical Statistics 31 (1), 254-262, 2022 | 48 | 2022 |
Sparse projection oblique randomer forests TM Tomita, J Browne, C Shen, J Chung, JL Patsolic, B Falk, CE Priebe, ... Journal of machine learning research 21 (104), 1-39, 2020 | 47* | 2020 |
The exact equivalence of distance and kernel methods in hypothesis testing C Shen, JT Vogelstein AStA Advances in Statistical Analysis 105, 385-403, 2021 | 42 | 2021 |
Generalized canonical correlation analysis for classification C Shen, M Sun, M Tang, CE Priebe Journal of Multivariate Analysis 130, 310-322, 2014 | 41 | 2014 |
Discovering and deciphering relationships across disparate data modalities JT Vogelstein, EW Bridgeford, Q Wang, CE Priebe, M Maggioni, C Shen Elife 8, e41690, 2019 | 39* | 2019 |
Network dependence testing via diffusion maps and distance-based correlations Y Lee, C Shen, CE Priebe, JT Vogelstein Biometrika 106 (4), 857-873, 2019 | 25 | 2019 |
Robust vertex classification L Chen, C Shen, JT Vogelstein, CE Priebe IEEE transactions on pattern analysis and machine intelligence 38 (3), 578-590, 2015 | 20 | 2015 |
hyppo: A multivariate hypothesis testing Python package S Panda, S Palaniappan, J Xiong, EW Bridgeford, R Mehta, C Shen, ... arXiv preprint arXiv:1907.02088, 2019 | 19* | 2019 |
Manifold matching using shortest-path distance and joint neighborhood selection C Shen, JT Vogelstein, CE Priebe Pattern Recognition Letters 92, 41-48, 2017 | 17 | 2017 |
Sparse representation classification beyond ℓ1 minimization and the subspace assumption C Shen, L Chen, Y Dong, CE Priebe IEEE Transactions on Information Theory 66 (8), 5061-5071, 2020 | 16 | 2020 |
Nonpar manova via independence testing S Panda, C Shen, R Perry, J Zorn, A Lutz, CE Priebe, JT Vogelstein arXiv e-prints, arXiv: 1910.08883, 2019 | 16* | 2019 |
Discovering the Signal Subgraph: An Iterative Screening Approach on Graphs C Shen, S Wang, A Badea, CE Priebe, JT Vogelstein arXiv e-prints, arXiv: 1801.07683, 2018 | 14* | 2018 |
One-Hot Graph Encoder Embedding C Shen, Q Wang, CE Priebe IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (6), 7933 …, 2023 | 10 | 2023 |
Estimating information-theoretic quantities with uncertainty forests R Guo, R Mehta, J Arroyo, H Helm, C Shen, JT Vogelstein arXiv, arXiv: 1907.00325, 2019 | 10* | 2019 |
Independence testing for temporal data C Shen, J Chung, R Mehta, T Xu, JT Vogelstein arXiv e-prints, arXiv: 1908.06486, 2019 | 7* | 2019 |
On the incommensurability phenomenon DE Fishkind, C Shen, Y Park, CE Priebe Journal of Classification 33, 185-209, 2016 | 6 | 2016 |
A simple spectral failure mode for graph convolutional networks CE Priebe, C Shen, N Huang, T Chen IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (11), 8689 …, 2021 | 5 | 2021 |
Graph independence testing J Xiong, C Shen, J Arroyo, JT Vogelstein arXiv preprint arXiv:1906.03661, 2019 | 5 | 2019 |
Decision forests induce characteristic kernels C Shen, JT Vogelstein arXiv preprint arXiv:1812.00029, 2018 | 4 | 2018 |