1.5k citations · 3.2k across the 70 of their papers we have counts for
44 papers · 1 filter
The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R
Xingguo Li, Tuo Zhao, Xiaoming Yuan +1
This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, Lasso, and Dantzig selector) an…
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
Jason Ge, Xingguo Li, Haoming Jiang +4
We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear reg…
The huge Package for High-dimensional Undirected Graph Estimation in R
Tuo Zhao, Han Liu, Kathryn Roeder +2
We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the…
A convex formulation for high-dimensional sparse sliced inverse regression
Kean Ming Tan, Zhaoran Wang, Tong Zhang +2
Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of informatio…
Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models
Carson Eisenach, Han Liu
Motivated by the task of clustering either variables or points into groups, we investigate efficient algorithms to solve the Peng-Wei (P-W) -means semi-definite prog…
Max-Norm Optimization for Robust Matrix Recovery
Ethan X. Fang, Han Liu, Kim-Chuan Toh +1
This paper studies the matrix completion problem under arbitrary sampling schemes. We propose a new estimator incorporating both max-norm and nuclear-norm regularization, based on…
Sharp Computational-Statistical Phase Transitions via Oracle Computational Model
Zhaoran Wang, Quanquan Gu, Han Liu
We study the fundamental tradeoffs between computational tractability and statistical accuracy for a general family of hypothesis testing problems with combinatorial structures. Ba…
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
Junwei Lu, Mladen Kolar, Han Liu
We propose a novel class of time-varying nonparanormal graphical models, which allows us to model high dimensional heavy-tailed systems and the evolution of their latent network st…
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
Zhuoran Yang, Zhaoran Wang, Han Liu +2
We study parameter estimation and asymptotic inference for sparse nonlinear regression. More specifically, we assume the data are given by , where is n…
Optimal linear estimation under unknown nonlinear transform
Xinyang Yi, Zhaoran Wang, Constantine Caramanis +1
Linear regression studies the problem of estimating a model parameter , from observations from linear model $y_i = \langl…
Graphical Fermat's Principle and Triangle-Free Graph Estimation
Junwei Lu, Han Liu
We consider the problem of estimating undirected triangle-free graphs of high dimensional distributions. Triangle-free graphs form a rich graph family which allows arbitrary loopy…
The Knowledge Gradient Policy Using A Sparse Additive Belief Model
Yan Li, Han Liu, Warren Powell
We propose a sequential learning policy for noisy discrete global optimization and ranking and selection (R\&S) problems with high dimensional sparse belief functions, where there…
Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model
Junwei Lu, Mladen Kolar, Han Liu
We develop a novel procedure for constructing confidence bands for components of a sparse additive model. Our procedure is based on a new kernel-sieve hybrid estimator that combine…
Local and Global Inference for High Dimensional Nonparanormal Graphical Models
Quanquan Gu, Yuan Cao, Yang Ning +1
This paper proposes a unified framework to quantify local and global inferential uncertainty for high dimensional nonparanormal graphical models. In particular, we consider the pro…
Provable Sparse Tensor Decomposition
Will Wei Sun, Junwei Lu, Han Liu +1
We propose a novel sparse tensor decomposition method, namely Tensor Truncated Power (TTP) method, that incorporates variable selection into the estimation of decomposition compone…
High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality
Zhaoran Wang, Quanquan Gu, Yang Ning +1
We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. In particular, we make two contributions: (i) For…
A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models
Yang Ning, Han Liu
We consider the problem of uncertainty assessment for low dimensional components in high dimensional models. Specifically, we propose a decorrelated score function to handle the im…
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…