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Monotonic Gaussian process flow

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Monotonic Gaussian process flow


Carl Heinrik Ek


Gaussian processes are stochastic processes that allows for a Bayesian treatment over the space of functions. In this talk I will briefly introduce Bayesian non-parametrics and Gaussian processes in specific. I will then describe recent work using Gaussian processes in the formulation of stochastic differential equations. In specific I will focus on how we can construct distributions over monotonic functions. I will show how these structures can be used to learn hierarchically decomposed uncertanties in composite models. Permitting time and interest I will also go through how we can perform tractable inference by formulating avariational lower bound on the marginal loglikelihood.


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.


2020-01-16 at 10:00 am (subject to variability)