| A road to classification in high dimensional space: the regularized optimal affine discriminant J Fan, Y Feng, X Tong Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2012 | 218 | 2012 |
| Neyman-pearson classification, convexity and stochastic constraints P Rigollet, X Tong Journal of machine learning research, 2011 | 119 | 2011 |
| Neyman-Pearson classification algorithms and NP receiver operating characteristics X Tong, Y Feng, JJ Li Science advances 4 (2), eaao1659, 2018 | 77 | 2018 |
| A survey on Neyman‐Pearson classification and suggestions for future research X Tong, Y Feng, A Zhao Wiley Interdisciplinary Reviews: Computational Statistics 8 (2), 64-81, 2016 | 58 | 2016 |
| A plug-in approach to neyman-pearson classification X Tong The Journal of Machine Learning Research 14 (1), 3011-3040, 2013 | 54 | 2013 |
| Statistical hypothesis testing versus machine learning binary classification: Distinctions and guidelines JJ Li, X Tong Patterns 1 (7), 2020 | 53 | 2020 |
| Feature augmentation via nonparametrics and selection (FANS) in high-dimensional classification J Fan, Y Feng, J Jiang, X Tong Journal of the American Statistical Association 111 (513), 275-287, 2016 | 47 | 2016 |
| Imbalanced classification: A paradigm‐based review Y Feng, M Zhou, X Tong Statistical Analysis and Data Mining: The ASA Data Science Journal 14 (5 …, 2021 | 29 | 2021 |
| Neyman-Pearson classification under high-dimensional settings A Zhao, Y Feng, L Wang, X Tong Journal of Machine Learning Research 17 (212), 1-39, 2016 | 24 | 2016 |
| Eigen selection in spectral clustering: a theory-guided practice X Han, X Tong, Y Fan Journal of the American Statistical Association 118 (541), 109-121, 2023 | 20 | 2023 |
| A burden shared is a burden halved: A fairness-adjusted approach to classification B Rava, W Sun, GM James, X Tong arXiv preprint arXiv:2110.05720, 2021 | 16 | 2021 |
| Neyman-Pearson classification: parametrics and sample size requirement X Tong, L Xia, J Wang, Y Feng Journal of Machine Learning Research 21 (12), 1-48, 2020 | 15 | 2020 |
| Imbalanced classification: an objective-oriented review Y Feng, M Zhou, X Tong arXiv preprint arXiv:2002.04592, 2020 | 13 | 2020 |
| Intentional control of type I error over unconscious data distortion: A Neyman–Pearson approach to text classification L Xia, R Zhao, Y Wu, X Tong Journal of the American Statistical Association 116 (533), 68-81, 2021 | 9 | 2021 |
| AIDE: annotation-assisted isoform discovery with high precision WV Li, S Li, X Tong, L Deng, H Shi, JJ Li Genome research 29 (12), 2056-2072, 2019 | 9 | 2019 |
| Multi-agent inference in social networks: a finite population learning approach J Fan, X Tong, Y Zeng Journal of the American Statistical Association 110 (509), 149-158, 2015 | 9* | 2015 |
| Penalized least squares estimation with weakly dependent data JQ Fan, L Qi, X Tong Science China Mathematics 59, 2335-2354, 2016 | 8 | 2016 |
| Adaptive conformal classification with noisy labels M Sesia, YX Wang, X Tong arXiv preprint arXiv:2309.05092, 2023 | 6 | 2023 |
| Neyman-pearson classification under high-dimensional settings A Zhao, Y Feng, L Wang, X Tong arXiv preprint arXiv:1508.03106, 2015 | 6 | 2015 |
| A flexible model-free prediction-based framework for feature ranking JJ Li, YE Chen, X Tong Journal of Machine Learning Research 22 (124), 1-54, 2021 | 5 | 2021 |
Yang FengProfessor of Biostatistics, School of Global Public Health, New York UniversityE-mail confirmado em nyu.edu
