9 citations · 16 across the 4 of their papers we have counts for
4 papers
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
Tim Tsz-Kit Lau, Han Liu, Thomas Pock
We study the problem of approximate sampling from non-log-concave distributions, e.g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodali…
Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes
Tim Tsz-Kit Lau, Han Liu
We propose efficient Langevin Monte Carlo algorithms for sampling distributions with nonsmooth convex composite potentials, which is the sum of a continuously differentiable functi…
Graphical Nonconvex Optimization for Optimal Estimation in Gaussian Graphical Models
Qiang Sun, Kean Ming Tan, Han Liu +1
We consider the problem of learning high-dimensional Gaussian graphical models. The graphical lasso is one of the most popular methods for estimating Gaussian graphical models. How…
A Likelihood Ratio Framework for High Dimensional Semiparametric Regression
Yang Ning, Tianqi Zhao, Han Liu
We propose a likelihood ratio based inferential framework for high dimensional semiparametric generalized linear models. This framework addresses a variety of challenging problems…