1.5k citations · 3.2k across the 70 of their papers we have counts for
10 papers · 2 filters
Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification
Robert Vanderbei, Han Liu, Lie Wang +1
In this paper we present two new approaches to efficiently solve large-scale compressed sensing problems. These two ideas are independent of each other and can therefore be used ei…
Joint Estimation of Multiple Graphical Models from High Dimensional Time Series
Huitong Qiu, Fang Han, Han Liu +1
In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which…
ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions
Fang Han, Han Liu
We present a robust alternative to principal component analysis (PCA) --- called elliptical component analysis (ECA) --- for analyzing high dimensional, elliptically distributed da…
Challenges of Big Data Analysis
Jianqing Fan, Fang Han, Han Liu
Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heter…
A Direct Estimation of High Dimensional Stationary Vector Autoregressions
Fang Han, Huanran Lu, Han Liu
The vector autoregressive (VAR) model is a powerful tool in modeling complex time series and has been exploited in many fields. However, fitting high dimensional VAR model poses so…
Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models
Zhaoran Wang, Fang Han, Han Liu
We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension to scale wit…
Optimal Feature Selection in High-Dimensional Discriminant Analysis
Mladen Kolar, Han Liu
We consider the high-dimensional discriminant analysis problem. For this problem, different methods have been proposed and justified by establishing exact convergence rates for the…
Optimal computational and statistical rates of convergence for sparse nonconvex learning problems
Zhaoran Wang, Han Liu, Tong Zhang
We provide theoretical analysis of the statistical and computational properties of penalized -estimators that can be formulated as the solution to a possibly nonconvex optimizat…
Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution
Fang Han, Han Liu
Correlation matrices play a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlati…
Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery
Han Liu, Lie Wang, Tuo Zhao
We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regular…