280 citations · 2k across the 81 of their papers we have counts for
8 papers · 2 filters
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…
Meta-Learning Probabilistic Inference For Prediction
Jonathan Gordon, John Bronskill, Matthias Bauer +2
This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic…
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Aapo Hyvarinen, Hiroaki Sasaki, Richard E. Turner
Nonlinear ICA is a fundamental problem for unsupervised representation learning, emphasizing the capacity to recover the underlying latent variables generating the data (i.e., iden…
Gaussian Process Behaviour in Wide Deep Neural Networks
Alexander G. de G. Matthews, Mark Rowland, Jiri Hron +2
Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relation…
Learning Causally-Generated Stationary Time Series
Wessel Bruinsma, Richard E. Turner
We present the Causal Gaussian Process Convolution Model (CGPCM), a doubly nonparametric model for causal, spectrally complex dynamical phenomena. The CGPCM is a generative model i…
Conditional Density Estimation with Bayesian Normalising Flows
Brian L Trippe, Richard E Turner
Modeling complex conditional distributions is critical in a variety of settings. Despite a long tradition of research into conditional density estimation, current methods employ ei…
The Gaussian Process Autoregressive Regression Model (GPAR)
James Requeima, Will Tebbutt, Wessel Bruinsma +1
Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically y…
Overpruning in Variational Bayesian Neural Networks
Brian Trippe, Richard Turner
The motivations for using variational inference (VI) in neural networks differ significantly from those in latent variable models. This has a counter-intuitive consequence; more ex…