It is difficult to use subsampling with variational inference in hierarchical models since the
number of local latent variables scales with the dataset. Thus, inference in hierarchical …

We develop nested variational inference (NVI), a family of methods that learn proposals for
nested importance samplers by minimizing an forward or reverse KL divergence at each …

Amortized variational inference (A-VI) is a method for approximating the intractable posterior
distributions that arise in probabilistic models. The defining feature of A-VI is that it learns a …

Abstract Stochastic gradient Markov Chain Monte Carlo (SGMCMC) is a scalable algorithm
for asymptotically exact Bayesian inference in parameter-rich models, such as Bayesian …

Abstract We introduce Support Decomposition Variational Inference (SDVI), a new
variational inference (VI) approach for probabilistic programs with stochastic support …

Estimating the parameters of a probabilistic directed graphical model from incomplete data
remains a long-standing challenge. This is because, in the presence of latent variables, both …

Federated learning methods enable model training across distributed data sources without
data leaving their original locations and have gained increasing interest in various fields …

The automation of probabilistic reasoning is one of the primary aims of machine learning.
Recently, the confluence of variational inference and deep learning has led to powerful and …

Normalizing flow-based variational inference (flow VI) is a promising approximate inference
approach, but its performance remains inconsistent across studies. Numerous algorithmic …

Probabilistic programming frameworks are powerful tools for statistical modelling and
inference. They are not immediately generalisable to phylogenetic problems due to the …