2 papers
cs.LG2019
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…
stat.CO2018
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…