64 citations · 192 across the 29 of their papers we have counts for
5 papers · 1 filter
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…
Monte Carlo Approximation of Bayes Factors via Mixing with Surrogate Distributions
Chenguang Dai, Jun S. Liu
By mixing the target posterior distribution with a surrogate distribution, of which the normalizing constant is tractable, we propose a method for estimating the marginal likelihoo…
The Wang-Landau Algorithm as Stochastic Optimization and Its Acceleration
Chenguang Dai, Jun S. Liu
We show that the Wang-Landau algorithm can be formulated as a stochastic gradient descent algorithm minimizing a smooth and convex objective function, of which the gradient is esti…
Generative Parameter Sampler For Scalable Uncertainty Quantification
Minsuk Shin, Young Lee, Jun S. Liu
Uncertainty quantification has been a core of the statistical machine learning, but its computational bottleneck has been a serious challenge for both Bayesians and frequentists. W…