64 citations · 192 across the 29 of their papers we have counts for
13 papers · 1 filter
Free-support Wasserstein Resampling in Sequential Monte Carlo
Mengyu Li, Cheng Meng, Jun S. Liu
In modern sequential Monte Carlo (SMC) applications, such as diffusion model samplers, propagating a particle is expensive, so only a few particles can be afforded. In this regime,…
Monotone Cubic B-Splines with a Neural-Network Generator
Lijun Wang, Xiaodan Fan, Huabai Li +1
We present a method for fitting monotone curves using cubic B-splines, which is equivalent to putting a monotonicity constraint on the coefficients. We explore different ways of en…
Varying Coefficient Model via Adaptive Spline Fitting
Xufei Wang, Bo Jiang, Jun S. Liu
The varying coefficient model has received broad attention from researchers as it is a powerful dimension reduction tool for non-parametric modeling. Most existing varying coeffici…
A Scale-free Approach for False Discovery Rate Control in Generalized Linear Models
Chenguang Dai, Buyu Lin, Xin Xing +1
The generalized linear models (GLM) have been widely used in practice to model non-Gaussian response variables. When the number of explanatory features is relatively large, scienti…
Generative Multiple-purpose Sampler for Weighted M-estimation
Minsuk Shin, Shijie Wang, Jun S Liu
To overcome the computational bottleneck of various data perturbation procedures such as the bootstrap and cross validations, we propose the Generative Multiple-purpose Sampler (GM…
False Discovery Rate Control via Data Splitting
Chenguang Dai, Buyu Lin, Xin Xing +1
Selecting relevant features associated with a given response variable is an important issue in many scientific fields. Quantifying quality and uncertainty of a selection result via…
Controlling False Discovery Rate Using Gaussian Mirrors
Xin Xing, Zhigen Zhao, Jun S. Liu
Simultaneously finding multiple influential variables and controlling the false discovery rate (FDR) for linear regression models is a fundamental problem. We here propose the Gaus…
Minimax Nonparametric Two-sample Test under Smoothing
Xin Xing, Zuofeng Shang, Pang Du +3
We consider the problem of comparing probability densities between two groups. A new probabilistic tensor product smoothing spline framework is developed to model the joint density…
Bayesian Analysis of Rank Data with Covariates and Heterogeneous Rankers
Xinran Li, Dingdong Yi, Jun S. Liu
Data in the form of ranking lists are frequently encountered, and combining ranking results from different sources can potentially generate a better ranking list and help understan…
Bayesian nonparametric tests via sliced inverse modeling
Bo Jiang, Chao Ye, Jun S. Liu
We study the problem of independence and conditional independence tests between categorical covariates and a continuous response variable, which has an immediate application in gen…
Variable selection for general index models via sliced inverse regression
Bo Jiang, Jun S. Liu
Variable selection, also known as feature selection in machine learning, plays an important role in modeling high dimensional data and is key to data-driven scientific discoveries.…
Lookahead Strategies for Sequential Monte Carlo
Ming Lin, Rong Chen, Jun S. Liu
Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic s…
The EM Algorithm and the Rise of Computational Biology
Xiaodan Fan, Yuan Yuan, Jun S. Liu
In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagin…