Machine-Learning

Huawei-Edinburgh Joint Lab: Discrete Codebook World Models

Nordic AI Meet & AI Day: Sample-efficient Reinforcement Learning with Implicitly Quantized Representations

iQRL: Implicitly Quantized Representations for Sample-Efficient Reinforcement Learning

I will be presenting our research on self-supervised representation learning for reinforcement learning at the [International Workshop of Intelligent Autonomous Learning Systems …

Model-Based Reinforcement Learning

I'll be giving a lecture on model-based RL at the Cambridge Ellis Unit Summer School on Probabilistic Machine Learning 2024.

Function-space Parameterization of Neural Networks for Sequential Learning

Implicitly Quantized Representations for Reinforcement Learning

Learning representations for reinforcement learning (RL) has shown much promise for continuous control. In this project, we investigate using vector quantization to prevent …

Function-Space Bayesian Deep Learning for Sequential Learning

Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes …

(Function-space) Laplace Approximation for Bayesian Neural Networks

Neural Networks as Sparse Gaussian Processes for Sequential Learning

I will be presenting our research on bayesian deep learning for sequential learning at the [International Workshop of Intelligent Autonomous Learning Systems …

Sparse Function-space Representation of Neural Networks