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
Aidan Scannell
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1 min readIdentifiable Mixtures of Sparse Variational Gaussian Process Experts
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 …
Gaussian Process Regression
This post introduces the theory underpinning Gaussian process regression and provides a basic walk-through in python.