Yang Feng

708 Broadway, Room 433

Department of Biostatistics

New York University

New York, NY 10003

Email: yf31@nyu.edu

Yang Feng is a Professor of Biostatistics at the School of Global Public Health, while also serving as an affiliate faculty member at the Center for Data Science and PRIISM, at New York University.

Feng’s research interests encompass the theoretical and methodological aspects of machine learning, high-dimensional statistics, network models, and nonparametric statistics, leading to a wealth of practical applications. He has published over 70 peer-reviewed articles with over 4,000 Google Scholar Citations. He is a fellow of the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS) and an elected member of the International Statistical Institute (ISI).

He is currently an associate editor for

His research is partially supported by

NSF Grant DMS-2324489: Collaborative Research: New Theory and Methods for High-Dimensional Multi-Task and Transfer Learning Inference

My Google Scholar Page (By Year)

A Short CV

latest posts

Dec 04, 2024	My Spanish Learning Journey

selected publications

Neyman-pearson multi-class classification via cost-sensitive learning

Ye Tian, and Yang Feng

Journal of the American Statistical Association, 2024

DOI Bib

@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},
}

Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms

Ye Tian, Haolei Weng, and Yang Feng

In Forty-first International Conference on Machine Learning, 2024

arXiv Bib

@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},
}

Transfer learning under high-dimensional generalized linear models

Ye Tian, and Yang Feng

Journal of the American Statistical Association, 2023

Bib PDF

@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},
}

DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation

Yifan He, Ruiyang Wu, Yong Zhou, and Yang Feng

Journal of the American Statistical Association, 2023

Bib PDF

@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},
}

PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection

Sihan Huang, Jiajin Sun, and Yang Feng

Journal of the American Statistical Association, 2023

Bib PDF

@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},
}

Testing community structure for hypergraphs

Mingao Yuan, Ruiqi Liu, Yang Feng, and Zuofeng Shang

The Annals of Statistics, 2022

Bib PDF

@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},
}

Large-scale model selection in misspecified generalized linear models

Emre Demirkaya, Yang Feng, Pallavi Basu, and Jinchi Lv

Biometrika, 2022

Bib PDF

@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},
}

RaSE: Random Subspace Ensemble Classification

Ye Tian, and Yang Feng

Journal of Machine Learning Research, 2021

Bib PDF

@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},
}

RaSE: A Variable Screening Framework via Random Subspace Ensembles

Ye Tian, and Yang Feng

Journal of American Statistical Association, 2021

Bib PDF

@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},
}

A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models

Jianqing Fan, Yang Feng, and Lucy Xia

Journal of Econometrics, 2020

Bib PDF

@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},
}

Neyman-Pearson classification: parametrics and sample size requirement

Xin Tong, Lucy Xia, Jiacheng Wang, and Yang Feng

Journal of Machine Learning Research, 2020

Bib PDF

@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},
}

A kronecker product model for repeated pattern detection on 2d urban images

Juan Liu, Emmanouil Z Psarakis, Yang Feng, and Ioannis Stamos

IEEE transactions on pattern analysis and machine intelligence, 2019

Bib PDF Supp

@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},
}

Neyman-Pearson classification algorithms and NP receiver operating characteristics

Xin Tong, Yang Feng, and Jingyi Jessica Li

Science Advances, 2018

Bib PDF

@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},
}

Model selection for high-dimensional quadratic regression via regularization

Ning Hao, Yang Feng, and Hao Helen Zhang

Journal of the American Statistical Association, 2018

Bib PDF

@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},
}

Neyman-Pearson classification under high-dimensional settings

Anqi Zhao, Yang Feng, Lie Wang, and Xin Tong

Journal of Machine Learning Research, 2016

Bib PDF

@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},
}

Nonparametric independence screening in sparse ultra-high-dimensional additive models

Jianqing Fan, Yang Feng, and Rui Song

Journal of the American Statistical Association, 2011

Bib PDF

@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},
}