4 papers
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology
Neil K. Chada, Simon Cotter, Kody J. H. Law +1
Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by representing unknown quantities t…
Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
Xinzhu Liang, Joseph M. Lukens, Sanjaya Lohani +4
This work introduces a new method designed for Bayesian deep learning called scalable Bayesian Monte Carlo (SBMC). The method is comprised of a model and an algorithm. The model in…
Comparison of parallel SMC and MCMC for Bayesian deep learning
Xinzhu Liang, Joseph M. Lukens, Sanjaya Lohani +4
This work systematically compares parallel implementations of consistent (asymptotically unbiased) Bayesian deep learning algorithms: sequential Monte Carlo sampler (SMC$_\parallel…
A randomized multi-index sequential Monte Carlo method
Xinzhu Liang, Shangda Yang, Simon L. Cotter +1
We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be ap…