Bayesian-Inference

Function-space Parameterization of Neural Networks for Sequential Learning

Sparse Function-space Representation of Neural Networks

Mode-constrained Model-based Reinforcement Learning via Gaussian Processes

We present a model-based RL algorithm that constrains training to a single dynamic mode with high probability. This is a difficult problem because the mode constraint is a hidden …

Model-based reinforcement learning under uncertainty

Identifiable Mixtures of Sparse Variational Gaussian Process Experts

Aidan Scannell

• 26 May, 2021 • 1 min read

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Identifiable Mixtures of Sparse Variational Gaussian Process Experts

This work introduces a variational lower bound for the Mixture of Gaussian Process Experts model with a GP-based gating network based on sparse GPs. The model (and inference) are …

Aidan Scannell

• 16 Nov, 2020 • 1 min read

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Gaussian Process Regression

This post introduces the theory underpinning Gaussian process regression and provides a basic walk-through in python.

Aidan Scannell

• 1 Oct, 2019 • 22 min read

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