Abstract
In this talk, I provide my perspective on the machine learning community’s efforts to develop inference procedures with Bayesian characteristics that go beyond Bayes' Rule as an epistemological principle. I will explain why these efforts are needed, as well as the forms which they take. Focusing on some of my own contributions to the field, I will trace out the community’s most important milestones, as well as the challenges that lie ahead. Throughout, I will provide success stories of the field, and emphasise the new opportunities that open themselves up to us once we dare to go beyond orthodox Bayesian procedures.
Jeremias Knoblauch
Associate Professor and EPSRC Fellow in Machine Learning & Statistics
My research interests include robust Bayesian methods, generalised and post-Bayesian methodology, variational methods, and simulators.