Yang Feng

25 catalog papers. Identity-linked publications and additional name matches are distinguished below.

ORCID: 0000-0001-7746-7598

New York University

Publications

• Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment — Journal of the American Statistical Association 2026
• Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness — Journal of Machine Learning Research 2025 (name-matched, not ORCID-linked)
• Neyman-Pearson Multi-Class Classification via Cost-Sensitive Learning — Journal of the American Statistical Association 2025
• Semiparametric modeling and analysis for longitudinal network data — The Annals of Statistics 2025
• DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation — Journal of the American Statistical Association 2024
• Yang Feng and Jiajin Sun’s contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’ by Crane and Xu — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2024
• PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection — Journal of the American Statistical Association 2024
• Transfer Learning Under High-Dimensional Generalized Linear Models — Journal of the American Statistical Association 2023
• RaSE: A Variable Screening Framework via Random Subspace Ensembles — Journal of the American Statistical Association 2023
• Testing community structure for hypergraphs — The Annals of Statistics 2022
• Large-scale model selection in misspecified generalized linear models — Biometrika 2022
• RaSE: Random Subspace Ensemble Classification — Journal of Machine Learning Research 2021 (name-matched, not ORCID-linked)
• The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID-19 Cases — Journal of the American Statistical Association 2021
• Neyman-Pearson classification: parametrics and sample size requirement — Journal of Machine Learning Research 2020 (name-matched, not ORCID-linked)
• Model Selection for High-Dimensional Quadratic Regression via Regularization — Journal of the American Statistical Association 2018
• Variable selection and prediction with incomplete high-dimensional data — The Annals of Applied Statistics 2016
• Neyman-Pearson Classification under High-Dimensional Settings — Journal of Machine Learning Research 2016 (name-matched, not ORCID-linked)
• Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification — Journal of the American Statistical Association 2016
• A road to classification in high dimensional space: the regularized optimal affine discriminant — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2012
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models — Journal of the American Statistical Association 2011
• Nonparametric estimation of genewise variance for microarray data — The Annals of Statistics 2010
• Comment — Journal of the American Statistical Association 2009
• Network exploration via the adaptive LASSO and SCAD penalties — The Annals of Applied Statistics 2009
• Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models — Journal of the American Statistical Association
• Variational Nonparametric Inference in Stochastic Block Models with Functional Covariates — Journal of the American Statistical Association