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03/28/2023
Sparse Gaussian Processes with Spherical Harmonic Features Revisited
We revisit the Gaussian process model with spherical harmonic features a...
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06/20/2022
Additive Gaussian Processes Revisited
Gaussian Process (GP) models are a class of flexible non-parametric mode...
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05/10/2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Deep Gaussian processes (DGPs) have struggled for relevance in applicati...
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04/12/2021
GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a ...
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06/30/2020
Sparse Gaussian Processes with Spherical Harmonic Features
We introduce a new class of inter-domain variational Gaussian processes ...
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03/09/2020
Amortized variance reduction for doubly stochastic objectives
Approximate inference in complex probabilistic models such as deep Gauss...
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03/02/2020
A Framework for Interdomain and Multioutput Gaussian Processes
One obstacle to the use of Gaussian processes (GPs) in large-scale probl...
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01/15/2020
Doubly Sparse Variational Gaussian Processes
The use of Gaussian process models is typically limited to datasets with...
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06/13/2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
We identify a new variational inference scheme for dynamical systems who...
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05/14/2019
Deep Gaussian Processes with Importance-Weighted Variational Inference
Deep Gaussian processes (DGPs) can model complex marginal densities as w...
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02/26/2019
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era
Banded matrices can be used as precision matrices in several models incl...
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02/15/2019
Translation Insensitivity for Deep Convolutional Gaussian Processes
Deep learning has been at the foundation of large improvements in image ...
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12/14/2018
Non-Factorised Variational Inference in Dynamical Systems
We focus on variational inference in dynamical systems where the discret...
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11/15/2018
Infinite-Horizon Gaussian Processes
Gaussian processes provide a flexible framework for forecasting, removin...
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10/30/2018
Gaussian Process Conditional Density Estimation
Conditional Density Estimation (CDE) models deal with estimating conditi...
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08/16/2018
Learning Invariances using the Marginal Likelihood
Generalising well in supervised learning tasks relies on correctly extra...
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04/03/2018
Large-Scale Cox Process Inference using Variational Fourier Features
Gaussian process modulated Poisson processes provide a flexible framewor...
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03/24/2018
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
The natural gradient method has been used effectively in conjugate Gauss...
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09/06/2017
Convolutional Gaussian Processes
We present a practical way of introducing convolutional structure into G...
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08/17/2017
Pseudo-extended Markov chain Monte Carlo
Sampling from the posterior distribution using Markov chain Monte Carlo ...
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08/16/2017
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction
Missing data and noisy observations pose significant challenges for reli...
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05/30/2017
Identification of Gaussian Process State Space Models
The Gaussian process state space model (GPSSM) is a non-linear dynamical...
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11/21/2016
Variational Fourier features for Gaussian processes
This work brings together two powerful concepts in Gaussian processes: t...
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10/27/2016
GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core c...
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04/18/2016
Chained Gaussian Processes
Gaussian process models are flexible, Bayesian non-parametric approaches...
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06/12/2015
MCMC for Variationally Sparse Gaussian Processes
Gaussian process (GP) models form a core part of probabilistic machine l...
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05/10/2015
Spike and Slab Gaussian Process Latent Variable Models
The Gaussian process latent variable model (GP-LVM) is a popular approac...
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04/27/2015
On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes
The variational framework for learning inducing variables (Titsias, 2009...
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12/03/2014
Nested Variational Compression in Deep Gaussian Processes
Deep Gaussian processes provide a flexible approach to probabilistic mod...
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11/07/2014
Scalable Variational Gaussian Process Classification
Gaussian process classification is a popular method with a number of app...
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10/18/2014
Gaussian Process Models with Parallelization and GPU acceleration
In this work, we present an extension of Gaussian process (GP) models wi...
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01/08/2014
Fast nonparametric clustering of structured time-series
In this publication, we combine two Bayesian non-parametric models: the ...
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09/26/2013
Gaussian Processes for Big Data
We introduce stochastic variational inference for Gaussian process model...
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06/22/2012