14 citations · 63 across the 16 of their papers we have counts for
19 papers
Signal-to-noise ratio aware minimax analysis of sparse linear regression
Shubhangi Ghosh, Yilin Guo, Haolei Weng +1
We consider parameter estimation under sparse linear regression -- an extensively studied problem in high-dimensional statistics and compressed sensing. While the minimax framework…
A note on the minimax risk of sparse linear regression
Yilin Guo, Shubhangi Ghosh, Haolei Weng +1
Sparse linear regression is one of the classical and extensively studied problems in high-dimensional statistics and compressed sensing. Despite the substantial body of literature…
Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms
Ye Tian, Haolei Weng, Yang Feng
While supervised federated learning approaches have enjoyed significant success, the domain of unsupervised federated learning remains relatively underexplored. Several federated E…
Post-Selection Inference for the Cox Model with Interval-Censored Data
Jianrui Zhang, Chenxi Li, Haolei Weng
We develop a post-selection inference method for the Cox proportional hazards model with interval-censored data, which provides asymptotically valid p-values and confidence interva…
Signal-to-noise ratio aware minimaxity and higher-order asymptotics
Yilin Guo, Haolei Weng, Arian Maleki
Since its development, the minimax framework has been one of the corner stones of theoretical statistics, and has contributed to the popularity of many well-known estimators, such…
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).…
Variable Selection in Latent Regression IRT Models via Knockoffs: An Application to International Large-scale Assessment in Education
Zilong Xie, Yunxiao Chen, Matthias von Davier +1
International large-scale assessments (ILSAs) play an important role in educational research and policy making. They collect valuable data on education quality and performance deve…
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…
Computing the degrees of freedom of rank-regularized estimators and cousins
Rahul Mazumder, Haolei Weng
Estimating a low rank matrix from its linear measurements is a problem of central importance in contemporary statistical analysis. The choice of tuning parameters for estimators re…
Does SLOPE outperform bridge regression?
Shuaiwen Wang, Haolei Weng, Arian Maleki
A recently proposed SLOPE estimator (arXiv:1407.3824) has been shown to adaptively achieve the minimax estimation rate under high-dimensional sparse linear regression mode…
Optimal estimation of functionals of high-dimensional mean and covariance matrix
Jianqing Fan, Haolei Weng, Yifeng Zhou
Motivated by portfolio allocation and linear discriminant analysis, we consider estimating a functional involving both the mean vector $\mathbfμ…
Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms
Rahul Mazumder, Diego F. Saldana, Haolei Weng
In this paper, we study the popularly dubbed matrix completion problem, where the task is to "fill in" the unobserved entries of a matrix from a small subset of observed entries, u…
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…
Which bridge estimator is optimal for variable selection?
Shuaiwen Wang, Haolei Weng, Arian Maleki
We study the problem of variable selection for linear models under the high-dimensional asymptotic setting, where the number of observations grows at the same rate as the numbe…
Low noise sensitivity analysis of Lq-minimization in oversampled systems
Haolei Weng, Arian Maleki
The class of Lq-regularized least squares (LQLS) are considered for estimating a p-dimensional vector \b{eta} from its n noisy linear observations y = X\b{eta}+w. The performance o…
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…
Overcoming The Limitations of Phase Transition by Higher Order Analysis of Regularization Techniques
Haolei Weng, Arian Maleki, Le Zheng
We study the problem of estimating from its noisy linear observations , where , under the following high-dimensional…
Does -minimization outperform -minimization?
Le Zheng, Arian Maleki, Haolei Weng +2
In many application areas we are faced with the following question: Can we recover a sparse vector from its undersampled set of noisy observations $y \in \ma…
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…