17 citations · 59 across the 15 of their papers we have counts for
17 papers
Is the regression -test doubly robust?
Lucy Xia, Oliver Y. Feng, Yang Feng +2
We study the robustness of the -test in random design linear models, and reach a somewhat nuanced conclusion. On the positive side, one of our main results is that the size of t…
Transfer Learning for Robust Structured Regression with Bi-level Source Detection
Haoming Shi, Yang Feng, Xiaoqian Liu
High-dimensional data in modern applications, such as COVID-19 mortality, often span multiple domains. Leveraging auxiliary information from source domains to improve performance i…
Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models
Ye Tian, Haolei Weng, Lucy Xia +1
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM).…
DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation
Yifan He, Ruiyang Wu, Yong Zhou +1
Distributed statistical learning has become a popular technique for large-scale data analysis. Most existing work in this area focuses on dividing the observations, but we propose…
Transfer Learning under High-dimensional Generalized Linear Models
Ye Tian, Yang Feng
In this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information f…
Machine Collaboration
Qingfeng Liu, Yang Feng
We propose a new ensemble framework for supervised learning, called machine collaboration (MaC), using a collection of base machines for prediction tasks. Unlike bagging/stacking (…
RaSE: A Variable Screening Framework via Random Subspace Ensembles
Ye Tian, Yang Feng
Variable screening methods have been shown to be effective in dimension reduction under the ultra-high dimensional setting. Most existing screening methods are designed to rank the…
Spectral clustering via adaptive layer aggregation for multi-layer networks
Sihan Huang, Haolei Weng, Yang Feng
One of the fundamental problems in network analysis is detecting community structure in multi-layer networks, of which each layer represents one type of edge information among the…
RaSE: Random Subspace Ensemble Classification
Ye Tian, Yang Feng
We propose a flexible ensemble classification framework, Random Subspace Ensemble (RaSE), for sparse classification. In the RaSE algorithm, we aggregate many weak learners, where e…
Nested Model Averaging on Solution Path for High-dimensional Linear Regression
Yang Feng, Qingfeng Liu
We study the nested model averaging method on the solution path for a high-dimensional linear regression problem. In particular, we propose to combine model averaging with regulari…
Large-Scale Model Selection with Misspecification
Emre Demirkaya, Yang Feng, Pallavi Basu +1
Model selection is crucial to high-dimensional learning and inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate…
Partial Distance Correlation Screening for High Dimensional Time Series
Kashif Yousuf, Yang Feng
High dimensional time series datasets are becoming increasingly common in various fields such as economics, finance, meteorology, and neuroscience. Given this ubiquity of time seri…
On the estimation of correlation in a binary sequence model
Haolei Weng, Yang Feng
We consider a binary sequence generated by thresholding a hidden continuous sequence. The hidden variables are assumed to have a compound symmetry covariance structure with a singl…
Community detection with nodal information
Haolei Weng, Yang Feng
Community detection is one of the fundamental problems in the study of network data. Most existing community detection approaches only consider edge information as inputs, and the…
Model Selection for High Dimensional Quadratic Regression via Regularization
Ning Hao, Yang Feng, Hao Helen Zhang
Quadratic regression (QR) models naturally extend linear models by considering interaction effects between the covariates. To conduct model selection in QR, it is important to main…
Model Selection in High-Dimensional Misspecified Models
Pallavi Basu, Yang Feng, Jinchi Lv
Model selection is indispensable to high-dimensional sparse modeling in selecting the best set of covariates among a sequence of candidate models. Most existing work assumes implic…
Regularization after retention in ultrahigh dimensional linear regression models
Haolei Weng, Yang Feng, Xingye Qiao
In ultrahigh dimensional setting, independence screening has been both theoretically and empirically proved a useful variable selection framework with low computation cost. In this…