Machine Learning

We illustrate how to build complicated probability distributions in a modular fashion using the Bijector API from TensorFlow Probability.

We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities from one domain as entities from …

Aboleth is a minimalistic TensorFlow framework for scalable Bayesian deep learning and Gaussian process approximation.

Determinant is a software service that makes predictions from sparse data, and learns what data it needs to optimise its performance.

Primary PhD research topic---expanding the scope and applicability of *variational inference* to encompass *implicit probabilistic models*.

An in-depth practical guide to variational encoders from a probabilistic perspective.

Revrand is a full-featured Python library for Bayesian generalized linear models, with random basis kernels for large-scale Gaussian process approximations.