Yang Feng

@article{he2023semiparametric,
  author = {He, Yinqiu and Sun, Jiajin and Tian, Yuang and Ying, Zhiliang and Feng, Yang},
  journal = {Annals of Statistics},
  title = {Semiparametric Modeling and Analysis for Longitudinal Network Data},
  year = {2025},
}

@article{tian2024neyman,
  author = {Tian, Ye and Feng, Yang},
  journal = {Journal of the American Statistical Association},
  title = {Neyman-pearson multi-class classification via cost-sensitive learning},
  year = {2024},
  number = {just-accepted},
  pages = {1--23},
  publisher = {Taylor \& Francis},
  arixv = {2111.04597},
  doi = {10.1080/01621459.2024.2402567},
}

@inproceedings{tian2024towards,
  author = {Tian, Ye and Weng, Haolei and Feng, Yang},
  booktitle = {Forty-first International Conference on Machine Learning},
  title = {Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms},
  year = {2024},
}

@article{tian2023transfer,
  author = {Tian, Ye and Feng, Yang},
  journal = {Journal of the American Statistical Association},
  title = {Transfer learning under high-dimensional generalized linear models},
  year = {2023},
  number = {544},
  pages = {2684--2697},
  volume = {118},
  publisher = {Taylor \& Francis},
}

@article{he2023ddac,
  author = {He, Yifan and Wu, Ruiyang and Zhou, Yong and Feng, Yang},
  journal = {Journal of the American Statistical Association},
  title = {DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation},
  year = {2023},
  pages = {1--12},
  publisher = {Taylor \& Francis},
}

@article{huang2023pcabm,
  author = {Huang, Sihan and Sun, Jiajin and Feng, Yang},
  journal = {Journal of the American Statistical Association},
  title = {PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection},
  year = {2023},
  pages = {1--13},
  publisher = {Taylor \& Francis},
}

@article{yuan2022testing,
  author = {Yuan, Mingao and Liu, Ruiqi and Feng, Yang and Shang, Zuofeng},
  journal = {The Annals of Statistics},
  title = {Testing community structure for hypergraphs},
  year = {2022},
  number = {1},
  pages = {147--169},
  volume = {50},
  publisher = {Institute of Mathematical Statistics},
}

@article{demirkaya2022large,
  author = {Demirkaya, Emre and Feng, Yang and Basu, Pallavi and Lv, Jinchi},
  journal = {Biometrika},
  title = {Large-scale model selection in misspecified generalized linear models},
  year = {2022},
  number = {1},
  pages = {123--136},
  volume = {109},
  publisher = {Oxford University Press},
}

@article{tian2021raseclassification,
  author = {Tian, Ye and Feng, Yang},
  journal = {Journal of Machine Learning Research},
  title = {RaSE: Random Subspace Ensemble Classification},
  year = {2021},
  date-modified = {2024-11-12 13:09:11 -0500},
  read = {0},
}

@article{tian2021rasescreening,
  author = {Tian, Ye and Feng, Yang},
  journal = {Journal of American Statistical Association},
  title = {RaSE: A Variable Screening Framework via Random Subspace Ensembles},
  year = {2021},
  date-modified = {2024-11-12 13:08:50 -0500},
}

@article{fan2020projection,
  author = {Fan, Jianqing and Feng, Yang and Xia, Lucy},
  journal = {Journal of Econometrics},
  title = {A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models},
  year = {2020},
}

@article{tong2020neyman,
  author = {Tong, Xin and Xia, Lucy and Wang, Jiacheng and Feng, Yang},
  journal = {Journal of Machine Learning Research},
  title = {Neyman-Pearson classification: parametrics and sample size requirement},
  year = {2020},
}

@article{liu2019kronecker,
  author = {Liu, Juan and Psarakis, Emmanouil Z and Feng, Yang and Stamos, Ioannis},
  journal = {IEEE transactions on pattern analysis and machine intelligence},
  title = {A kronecker product model for repeated pattern detection on 2d urban images},
  year = {2019},
  number = {9},
  pages = {2266--2272},
  volume = {41},
  publisher = {IEEE},
}

@article{tong2018neyman,
  author = {Tong, Xin and Feng, Yang and Li, Jingyi Jessica},
  journal = {Science Advances},
  title = {Neyman-Pearson classification algorithms and NP receiver operating characteristics},
  year = {2018},
  number = {2},
  pages = {eaao1659},
  volume = {4},
  publisher = {American Association for the Advancement of Science},
}

@article{hao2018model,
  author = {Hao, Ning and Feng, Yang and Zhang, Hao Helen},
  journal = {Journal of the American Statistical Association},
  title = {Model selection for high-dimensional quadratic regression via regularization},
  year = {2018},
  number = {522},
  pages = {615--625},
  volume = {113},
  publisher = {Taylor \& Francis},
}

@article{zhao2016neyman,
  author = {Zhao, Anqi and Feng, Yang and Wang, Lie and Tong, Xin},
  journal = {Journal of Machine Learning Research},
  title = {Neyman-Pearson classification under high-dimensional settings},
  year = {2016},
  number = {212},
  pages = {1--39},
  volume = {17},
}

@article{fan2011nonparametric,
  author = {Fan, Jianqing and Feng, Yang and Song, Rui},
  journal = {Journal of the American Statistical Association},
  title = {Nonparametric independence screening in sparse ultra-high-dimensional additive models},
  year = {2011},
  pages = {544--557},
  volume = {106},
  publisher = {Taylor \& Francis},
}