4 papers · 1 filter
Large-scale empirical tuning and comparison of default optimizers for variational inference
Trevor Campbell, Jonathan H. Huggins, Kyurae Kim +1
Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization. In practice, the stochastic optimizers underpinning BBVI…
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…
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…
Surrogate-Based Bayesian Inference: Uncertainty Quantification and Active Learning
Andrew Gerard Roberts, Michael C. Dietze, Jonathan H. Huggins
Surrogate models - also called emulators - are widely used to facilitate Bayesian inference in settings where computational costs preclude the use of standard posterior inference a…