4 papers
Bend to Mend: Toward Trustworthy Variational Bayes with Valid Uncertainty Quantification
Jiaming Liu, Meng Li
Variational Bayes (VB) is a popular and computationally efficient method to approximate the posterior distribution in Bayesian inference, especially when the exact posterior is ana…
Posterior Summarization for Variable Selection in Bayesian Tree Ensembles
Shengbin Ye, Meng Li
Variable selection remains a fundamental challenge in statistics, especially in nonparametric settings where model complexity can obscure interpretability. Bayesian tree ensembles,…
Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large
Shengbin Ye, Meng Li
Symbolic regression (SR) is a powerful technique for discovering symbolic expressions that characterize nonlinear relationships in data, gaining increasing attention for its interp…
Ranking Perspective for Tree-based Methods with Applications to Symbolic Feature Selection
Hengrui Luo, Meng Li
Tree-based methods are powerful nonparametric techniques in statistics and machine learning. However, their effectiveness, particularly in finite-sample settings, is not fully unde…