Xin Tong
Xin Tong
Assistant Professor, Department of Data Sciences and Operations, University of Southern California
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Citata da
Citata da
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
Neyman-pearson classification, convexity and stochastic constraints
P Rigollet, X Tong
Journal of Machine Learning Research, 2011
A plug-in approach to neyman-pearson classification
X Tong
The Journal of Machine Learning Research 14 (1), 3011-3040, 2013
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
Neyman-Pearson classification algorithms and NP receiver operating characteristics
X Tong, Y Feng, JJ Li
Science advances 4 (2), eaao1659, 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
Neyman-Pearson classification under high-dimensional settings
A Zhao, Y Feng, L Wang, X Tong
The Journal of Machine Learning Research 17 (1), 7469-7507, 2016
Penalized least squares estimation with weakly dependent data
JQ Fan, L Qi, X Tong
Science China Mathematics 59 (12), 2335-2354, 2016
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
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
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
Genomic applications of the Neyman-Pearson classification paradigm
JJ Li, X Tong
Big Data Analytics in Genomics, 2016
Neyman-Pearson classification under a strict constraint
P Rigollet, X Tong
Proceedings of the 24th Annual Conference on Learning Theory, 595-614, 2011
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, 1-14, 2020
Eigen selection in spectral clustering: a theory guided practice
X Han, X Tong, Y Fan
Journal of the American Statistical Association, 1-33, 2021
Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines
JJ Li, X Tong
Patterns 1 (7), 100115, 2020
A plug-in approach to anomaly detection
X Tong
Journal of Machine Learning Research 14, 3011-3040, 2013
Learning with asymmetry, high dimension and social networks
X Tong
Princeton, NJ: Princeton University, 2012
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