Thursday 03 May 2018Gaussian processes: models and inference

Dr. Carl Henrik Ek - University of Bristol

Harrison 170 14:30-15:30

*** 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-parametric which are methods that allow for unbounded model complexity and interpretable parametrisations. In this talk I will describe Gaussian and Dirichlet processes as examples of priors over infinite objects. 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 on with applications to airflow from windturbines and time-series data.

*** bio

Dr. Carl Henrik Ek is a senior lecturer at the University of Bristol. His research 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 robotics and computer vision.

Add to calendar

Add to calendar (.ics)