30 citations · 67 across the 5 of their papers we have counts for
5 papers · 1 filter
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Matthew Ashman, Thang D. Bui, Cuong V. Nguyen +4
The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional alg…
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Theofanis Karaletsos, Thang D. Bui
Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interfac…
Improving and Understanding Variational Continual Learning
Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui +1
In the continual learning setting, tasks are encountered sequentially. The goal is to learn whilst i) avoiding catastrophic forgetting, ii) efficiently using model capacity, and ii…
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop +1
Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. However, practitioners are faced with a fragmented literature that offers a…
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li +2
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wid…