49 citations · 131 across the 8 of their papers we have counts for
8 papers
Particle-based Online Bayesian Sampling
Yifan Yang, Chang Liu, Zheng Zhang
Online optimization has gained increasing interest due to its capability of tracking real-world streaming data. Although online optimization methods have been widely studied in the…
Sampling with Mirrored Stein Operators
Jiaxin Shi, Chang Liu, Lester Mackey
We introduce a new family of particle evolution samplers suitable for constrained domains and non-Euclidean geometries. Stein Variational Mirror Descent and Mirrored Stein Variatio…
An empirical -Wishart prior for sparse high-dimensional Gaussian graphical models
Chang Liu, Ryan Martin
In Gaussian graphical models, the zero entries in the precision matrix determine the dependence structure, so estimating that sparse precision matrix and, thereby, learning this un…
Bayesian inference in high-dimensional linear models using an empirical correlation-adaptive prior
Chang Liu, Yue Yang, Howard Bondell +1
In the context of a high-dimensional linear regression model, we propose the use of an empirical correlation-adaptive prior that makes use of information in the observed predictor…
Understanding and Accelerating Particle-Based Variational Inference
Chang Liu, Jingwei Zhuo, Pengyu Cheng +3
Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian inference literature, for their capacity to yield flexible and accurate approximations.…
Riemannian Stein Variational Gradient Descent for Bayesian Inference
Chang Liu, Jun Zhu
We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian inference method that generalizes Stein Variational Gradient Descent (SVGD) to Riemann manifold. The be…
Message Passing Stein Variational Gradient Descent
Jingwei Zhuo, Chang Liu, Jiaxin Shi +3
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation…
Robust High-Dimensional Linear Regression
Chang Liu, Bo Li, Yevgeniy Vorobeychik +1
The effectiveness of supervised learning techniques has made them ubiquitous in research and practice. In high-dimensional settings, supervised learning commonly relies on dimensio…