1.5k citations · 3.2k across the 70 of their papers we have counts for
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A General Framework for Robust Testing and Confidence Regions in High-Dimensional Quantile Regression
Tianqi Zhao, Mladen Kolar, Han Liu
We propose a robust inferential procedure for assessing uncertainties of parameter estimation in high-dimensional linear models, where the dimension can grow exponentially fast…
On Semiparametric Exponential Family Graphical Models
Zhuoran Yang, Yang Ning, Han Liu
We propose a new class of semiparametric exponential family graphical models for the analysis of high dimensional mixed data. Different from the existing mixed graphical models, we…
Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory
Tuo Zhao, Han Liu, Tong Zhang
The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the c…
Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functions
Mengdi Wang, Ethan X. Fang, Han Liu
Classical stochastic gradient methods are well suited for minimizing expected-value objective functions. However, they do not apply to the minimization of a nonlinear function invo…
Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time
Zhaoran Wang, Huanran Lu, Han Liu
Sparse principal component analysis (PCA) involves nonconvex optimization for which the global solution is hard to obtain. To address this issue, one popular approach is convex rel…
High Dimensional Semiparametric Latent Graphical Model for Mixed Data
Jianqing Fan, Han Liu, Yang Ning +1
Graphical models are commonly used tools for modeling multivariate random variables. While there exist many convenient multivariate distributions such as Gaussian distribution for…
High Dimensional Semiparametric Scale-Invariant Principal Component Analysis
Fang Han, Han Liu
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after uns…
Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning
Le Song, Han Liu, Ankur Parikh +1
Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer…