Abstract
Mixture models are inherently unidentifiable as different combinations of component distributions and mixture weights can generate the same distributions over the observations. We propose a scalable Mixture of Experts model where both the experts and gating functions are modelled using Gaussian processes. Importantly, this balanced treatment of the experts and the gating network introduces an interplay between the different parts of the model. This can be used to constrain the set of admissible functions reducing the identifiability issues normally associated with mixture models. The model resembles the original Mixture of Gaussian Process Experts method with a GP-based gating network. However, we derive a variational inference scheme that allows for stochastic updates enabling the model to be used in a more scalable fashion.
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Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Create your slides in Markdown - click the Slides button to check out the example.
Supplementary notes can be added here, including code, math, and images.
Aidan Scannell
Postdoctoral Researcher
My research interests include model-based reinforcement learning, probabilistic machine learning (gaussian processes, Bayesian neural networks, approximate Bayesian inference, etc), learning-based control and optimal control.