GPJax - Gaussian Processes in Jax
Minimal Gaussian process library in JAX with a simple (custom) approach to state management.
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Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation
Synergising Bayesian inference and Riemannian geometry for control in multimodal dynamical systems.
Synergising Bayesian Inference and Probabilistic Geometries for Robotic Control
Aidan Scannell
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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 …
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.
Probabilistic Modelling
I am in the process of creating Jupyter notebooks for several probabilistic models (Bayesian linear regression, Gaussian process regression) and approximate inference algorithms. …
Aidan Scannell
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Model-Based Reinforcement Learning with Gaussian Processes
In this work I re-implemented the PILCO algorithm in python using Tensorflow and GPflow. This work was mainly carried out for personal development and some of the implementation is …