Bayesian probabilistic modelling provides a principled framework for coherent inference and prediction under uncertainty. Approximate inference addresses the key challenge of Bayesian computation, that is, the computation of the intractable posterior distribution and related quantities such as the Bayesian predictive distribution. Significant progress has been made in this field during the past 10 years, which enables a wide application of Bayesian modelling techniques to machine learning tasks in computer vision, natural language processing, reinforcement learning etc.
This tutorial offers a coherent summary of the recent advances in approximate inference. We will start the tutorial with an introduction to the approximate inference concept and the basics in variational inference. Then we will describe the fundamental aspects of the modern approximate inference, including scalable inference, Monte Carlo techniques, amortized inference, approximate posterior design, and optimisation objectives. The connections between these recent advances will also be discussed. Lastly, we will provide application examples of advanced approximate inference techniques to downstream uncertainty estimation and decision-making tasks and conclude with a discussion on future research directions.
Timetable Tutorial part 1: basics of approximate inference (approx. 30min) Coffee break & live Q&A 1 (approx. 10min) Tutorial part 2: advances 1 (approx. 30min) Coffee break & live Q&A 2 (approx. 10min) Tutorial part 3: advances 2 (approx. 30min) Coffee break & live Q&A 3 (approx. 10min) Tutorial part 3: applications (approx. 30min)