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cs.LG2026
Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
Xiaoyu Wang, Jonathan H. Huggins
Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in latent variable models. How…
cs.LG2026
Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
Yu Wang, Jie Ding, Jonathan H. Huggins
Tuning algorithms such as stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD) for approximate sampling and uncertainty quantification remains challen…