Variational-Inference

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 …

PhD Thesis: Bayesian Learning for Control in Multimodal Dynamical Systems

Mode remaining navigation (and exploration) in unknown multimodal dynamical systems via model-based reinforcement learning.

Identifiable Mixtures of Sparse Variational Gaussian Process Experts

Aidan Scannell

• 26 May, 2021 • 1 min read

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Trajectory Optimisation in Learned Multimodal Dynamical Systems

This work presents a two-stage method to perform trajectory optimisation in multimodal dynamical systems with unknown nonlinear stochastic transition dynamics. The method finds …

Aidan Scannell

• 16 Nov, 2020 • 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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Approximate Inference

This work implements and compares a variety of approximate inference techniques for the tasks of image de-noising (restoration) and image segmentation.