1.5k citations · 3.2k across the 70 of their papers we have counts for
11 papers · 2 filters
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…