Seminar Details
2018-03-13 (14:00) : Approximate Bayesian inference in Machine Learning: where stats meets optimisation
At Euler building (room A.002)
Organized by Mathematical Engineering
Speaker :
Thibaut Lienart (University of Oxford)
Abstract :
In this talk I will discuss some recent work on distributed approximate Bayesian inference for Machine Learning where one is interested not only in finding good parameters for a given model (e.g.: the weights of a Neural Network) but also to model the uncertainty around these parameters and thereby model the uncertainty in the predictions made with the model.
This is of tremendous importance in modern applications of ML such as self-driving cars, healthcare applications etc.
I will show how approximate Bayesian inference converts a hard integration problem into a simpler optimisation problem and discuss some approaches that have been used to tackle the optimisation.
Finally, I will give a critical view on the field, hopefully leading to a discussion.