research
∙
08/27/2023
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
Sparse variational approximations are popular methods for scaling up inf...
share
research
∙
10/14/2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
As Gaussian processes mature, they are increasingly being deployed as pa...
research
∙
10/30/2020
Marginalised Gaussian Processes with Nested Sampling
Gaussian Process (GPs) models are a rich distribution over functions wit...
research
∙
10/07/2020
Ensembling geophysical models with Bayesian Neural Networks
Ensembles of geophysical models improve projection accuracy and express ...
research
∙
08/01/2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression
Gaussian processes are distributions over functions that are versatile a...
research
∙
06/23/2020
Variational Orthogonal Features
Sparse stochastic variational inference allows Gaussian process models t...
research
∙
12/31/2019
Approximate Inference for Fully Bayesian Gaussian Process Regression
Learning in Gaussian Process models occurs through the adaptation of hyp...
research
∙
12/18/2019
Benchmarking the Neural Linear Model for Regression
The neural linear model is a simple adaptive Bayesian linear regression ...
research
∙
06/13/2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
We identify a new variational inference scheme for dynamical systems who...
research
∙
02/04/2019
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Previously, the exploding gradient problem has been explained to be cent...
research
∙
12/14/2018
Non-Factorised Variational Inference in Dynamical Systems
We focus on variational inference in dynamical systems where the discret...
research
∙
12/10/2018
Closed-form Inference and Prediction in Gaussian Process State-Space Models
We examine an analytic variational inference scheme for the Gaussian Pro...
research
∙
08/16/2018
Deep Convolutional Networks as shallow Gaussian Processes
We show that the output of a (residual) convolutional neural network (CN...
research
∙
09/06/2017
Convolutional Gaussian Processes
We present a practical way of introducing convolutional structure into G...
research
∙
06/15/2016
Understanding Probabilistic Sparse Gaussian Process Approximations
Good sparse approximations are essential for practical inference in Gaus...
research
∙
02/08/2016
Data-Efficient Reinforcement Learning in Continuous-State POMDPs
We present a data-efficient reinforcement learning algorithm resistant t...
research
∙
02/10/2015
Gaussian Processes for Data-Efficient Learning in Robotics and Control
Autonomous learning has been a promising direction in control and roboti...
research
∙
02/24/2014
Manifold Gaussian Processes for Regression
Off-the-shelf Gaussian Process (GP) covariance functions encode smoothne...
research
∙
03/12/2013
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes
We introduce GP-FNARX: a new model for nonlinear system identification b...
research
∙
03/20/2012
Robust Filtering and Smoothing with Gaussian Processes
We propose a principled algorithm for robust Bayesian filtering and smoo...
research
∙
12/19/2011