We give an exact characterization of admissibility in statistical decision problems in terms of Bayes optimality in a so-called nonstandard extension of the original decision problem, as introduced by Duanmu and Roy. Unlike the consideration of …
In this talk I will discuss several recent papers that develop new graph neural networks by considering their relation to continuous processes. I will discuss how graph neural networks can be arrived at as numerical schemes to solve differential …
We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network. This method, referred to as functional regularisation for Continual Learning, avoids …
We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP method for multivariate data that scales linearly with respect to …
Reproducing kernel Hilbert spaces (RKHS) provide a powerful framework, termed kernel mean embeddings, for representing probability distributions, enabling nonparametric statistical inference in a variety of applications. Combining RKHS formalism with …
Arctic sea ice is a major component of the Earth’s climate system, as well as an integral platform for travel, subsistence, and habitat. Since the late 1970s, significant advancements have been made in our ability to closely monitor the state of the …
Neural networks and Gaussian processes represent different learning paradigms: the former are parametric and rely on ERM-based training, while the latter are non-parametric and employ Bayesian inference. Despite these differences, I will discuss how …
Downscaling aims to link the behaviour of the atmosphere at fine scales to properties measurable at coarser scales, and has the potential to provide high resolution information at a lower computational and storage cost than numerical simulation …
In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function evaluations. One example is budget constrained global optimization of f, for which Bayesian optimization is a popular …
We are happy to present this joint work with Mihaela Roșca, Răzvan Pascanu, Lucian Bușunoiu and Claudia Clopath on the effect of spectral normalisation in deep reinforcement learning. Most of the recent deep reinforcement learning advances take an …