machine-learning

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

Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining …

Learning representations for reinforcement learning (RL) has shown much promise for continuous control. In this project, we investigate using vector quantization to prevent representation collapse when learning representations for RL using a self-supervised latent-state consistency loss.

Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes elegantly tackle these problems, they struggle with scalability and handling rich inputs, such as images.

In this talk, I’ll present an overview of the Laplace approximation for quantifying uncertainty in Bayesian neural networks. …

Deep neural networks (NNs) are known to lack uncertainty estimates and struggle to incorporate new data. We present a method that …