27 citations · 36 across the 10 of their papers we have counts for
10 papers
Generative Quantile Regression with Variability Penalty
Shijie Wang, Minsuk Shin, Ray Bai
Quantile regression and conditional density estimation can reveal structure that is missed by mean regression, such as multimodality and skewness. In this paper, we introduce a dee…
Neural Bootstrapper
Minsuk Shin, Hyungjoo Cho, Hyun-seok Min +1
Bootstrapping has been a primary tool for ensemble and uncertainty quantification in machine learning and statistics. However, due to its nature of multiple training and resampling…
Bayesian Shrinkage for Functional Network Models, with Applications to Longitudinal Item Response Data
Jaewoo Park, Yeseul Jeon, Minsuk Shin +2
Longitudinal item response data are common in social science, educational science, and psychology, among other disciplines. Studying the time-varying relationships between items is…
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…
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…
Stochastic Approximation Hamiltonian Monte Carlo
Jonghyun Yun, Minsuk Shin, Ick Hoon Jin +1
Recently, the Hamilton Monte Carlo (HMC) has become widespread as one of the more reliable approaches to efficient sample generation processes. However, HMC is difficult to sample…
Neuronized Priors for Bayesian Sparse Linear Regression
Minsuk Shin, Jun S Liu
Although Bayesian variable selection methods have been intensively studied, their routine use in practice has not caught up with their non-Bayesian counterparts such as Lasso, like…
Functional Horseshoe Priors for Subspace Shrinkage
Minsuk Shin, Anirban Bhattacharya, Valen E. Johnson
We introduce a new shrinkage prior on function spaces, called the functional horseshoe prior (fHS), that encourages shrinkage towards parametric classes of functions. Unlike other…
Bayesian Variable Selection with Structure Learning: Applications in Integrative Genomics
Suprateek Kundu, Minsuk Shin, Yichen Cheng +3
Significant advances in biotechnology have allowed for simultaneous measurement of molecular data points across multiple genomic and transcriptomic levels from a single tumor/cance…
Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-Dimensional Settings
Minsuk Shin, Anirban Bhattacharya, Valen E. Johnson
Bayesian model selection procedures based on nonlocal alternative prior densities are extended to ultrahigh dimensional settings and compared to other variable selection procedures…