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Xin Tong
Xin Tong
Associate Professor, Department of Data Sciences and Operations, University of Southern California
Verified email at marshall.usc.edu - Homepage
Title
Cited by
Cited by
Year
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
2102012
Neyman-pearson classification, convexity and stochastic constraints
P Rigollet, X Tong
Journal of machine learning research, 2011
1092011
Neyman-Pearson classification algorithms and NP receiver operating characteristics
X Tong, Y Feng, JJ Li
Science advances 4 (2), eaao1659, 2018
742018
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
522016
A plug-in approach to neyman-pearson classification
X Tong
The Journal of Machine Learning Research 14 (1), 3011-3040, 2013
512013
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
462016
Statistical hypothesis testing versus machine learning binary classification: Distinctions and guidelines
JJ Li, X Tong
Patterns 1 (7), 2020
452020
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
242016
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
202021
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
152023
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
132021
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
122020
Imbalanced classification: an objective-oriented review
Y Feng, M Zhou, X Tong
arXiv preprint arXiv:2002.04592, 2020
112020
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
92019
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
82016
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
72021
Neyman-pearson classification under high-dimensional settings
A Zhao, Y Feng, L Wang, X Tong
arXiv preprint arXiv:1508.03106, 2015
62015
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
52021
Bridging cost-sensitive and neyman-pearson paradigms for asymmetric binary classification
WV Li, X Tong, JJ Li
arXiv preprint arXiv:2012.14951, 2020
42020
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