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Minsuk Shin
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Cited by
Year
Scalable Bayesian variable selection using nonlocal prior densities in ultrahigh-dimensional settings
M Shin, A Bhattacharya, VE Johnson
Statistica Sinica 28 (2), 1053, 2018
1002018
Functional Horseshoe Priors for Subspace Shrinkage
M Shin, A Bhattacharya, VE Johnson
arXiv preprint arXiv:1606.05021v3, 2018
262018
Kullback–Leibler information of a censored variable and its applications
S Park, M Shin
Statistics 48 (4), 756-765, 2014
212014
Neuronized Priors for Bayesian Sparse Linear Regression
M Shin, JS Liu
arXiv preprint arXiv:1810.00141, 2018
20*2018
Bayesian variable selection with graphical structure learning: Applications in integrative genomics
S Kundu, Y Cheng, M Shin, G Manyam, BK Mallick, ...
PloS ONE 13 (7), e0195070, 2018
112018
Neural bootstrapper
M Shin, H Cho, H Min, S Lim
Advances in Neural Information Processing Systems 34, 16596-16609, 2021
82021
Generative multiple-purpose sampler for weighted M-estimation
M Shin, S Wang, JS Liu
arXiv preprint arXiv:2006.00767, 2020
32020
Stochastic Approximation Hamiltonian Monte Carlo
IH Jin, M Shin, F Liang
arXiv preprint arXiv:1810.04811, https://arxiv.org/abs/1810.04811, 2018
32018
Generative quantile regression with variability penalty
S Wang, M Shin, R Bai
Journal of Computational and Graphical Statistics, 1-21, 2024
22024
Scalable uncertainty quantification via generative bootstrap sampler
M Shin, Y Lee, JS Liu
arXiv preprint arXiv:2006.00767, 2020
22020
Priors for bayesian shrinkage and high-dimensional model selection
M Shin
22017
Generative Multi-purpose Sampler for Weighted M-estimation
M Shin, S Wang, JS Liu
Journal of Computational and Graphical Statistics, 1-14, 2024
12024
Neural bootstrapping attention for neural processes
M Lee, J Park, S Jang, C Lee, H Cho, M Shin, S Lim
12021
Supplementary Material for “Generative Quantile Regression with Variability Penalty”
S Wang, M Shin, R Bai
2024
Fast Bootstrapping Nonparametric Maximum Likelihood for Latent Mixture Models
S Wang, M Shin, R Bai
arXiv preprint arXiv:2402.18748, 2024
2024
Bayesian Shrinkage for Functional Network Models, With Applications to Longitudinal Item Response Data
J Park, Y Jeon, M Shin, M Jeon, IH Jin
Journal of Computational and Graphical Statistics 31 (2), 360-377, 2022
2022
Bayesian Shrinkage for Functional Network Models with Intractable Normalizing Constants
J Park, Y Jeon, M Shin, M Jeon, IH Jin
arXiv preprint arXiv:2006.13698, 2020
2020
Generative Parameter Sampler For Scalable Uncertainty Quantification
M Shin, Y Lee, JS Liu
arXiv preprint arXiv:1905.12440, 2019
2019
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Articles 1–18