Machine Learning

A Handbook for Sparse Variational Gaussian Processes

A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework.

Density Ratio Estimation for KL Divergence Minimization between Implicit Distributions

This post demonstrates how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions, using density ratio estimation by probabilistic classification.

Building Probability Distributions with the TensorFlow Probability Bijector API

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

Contributed Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference

Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference

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 …

Variational Bayes for Implicit Probabilistic Models

Expanding the scope and applicability of variational inference to encompass implicit probabilistic models.


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.


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

A Tutorial on Variational Autoencoders with a Concise Keras Implementation

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