AASS Seminar - Composite Gaussian Processes

The research centre AASS arranges a seminar with Carl Henrik Ek, University of Bristol.

Abstract

The science of machine learning is concerned with developing tools that provides means to integrate our prior assumptions with observed data. A natural formalism of this is Bayesian non-parametrics which are methods that allow for unbounded model complexity and interpretable parametrisations. In this talk I will first give a brief introduction to stochastic processes, in specific Gaussian processes. I will describe how we can circmuvent the intractable inference by optimising a lower bound on the marginal likelihood. I will then exemplify the use of these model by showing recent work with applications to models of windturbines and time-series data.

Speaker's bio

Dr. Carl Henrik Ek is a senior lecturer at the University of Bristol. His reasearch focuses on developing computational models that allows machines to learn from data. In specific he is interested in Bayesian non-parametric models which allows for principled quantification of uncertainty, easy interpretability and adaptable complexity. He has worked extensively on models for representation learning with applications in automatic control, robotics and bioinformatics.