Workshop on Uncertainty Propagation in Composite Models

Workshop on Uncertainty Propagation in Composite Models

Abstract

Together with Carl Henrik Ek and Neill Campbell, I organized a workshop on uncertainty propagation in composite models at the Siemens AI Lab. This workshop will focused on three aspects of machine learning: (i) how to propagate uncertainty through composite models, (ii) how to interpret a composite probability distribution and (iii) how to evaluate success. Inspired by reinforcement learning we wanted to encourage a discussion and debate on what abstract tasks can be constructed that would provide an interpretable measure of success.

Type
Publication
Siemens AI Lab
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Markus Kaiser
PhD candidate in Bayesian Machine Learning

My research interests include hierarchical Bayesian modelling, Gaussian Processes and scalable Bayesian Inference.