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…
A Partially Linear Framework for Massive Heterogeneous Data
Tianqi Zhao, Guang Cheng, Han Liu
We consider a partially linear framework for modelling massive heterogeneous data. The major goal is to extract common features across all sub-populations while exploring heterogen…
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…