b'James Hensman'

research

∙ 03/28/2023

Sparse Gaussian Processes with Spherical Harmonic Features Revisited

We revisit the Gaussian process model with spherical harmonic features a...

1 Stefanos Eleftheriadis, et al. ∙

research

∙ 06/20/2022

Additive Gaussian Processes Revisited

Gaussian Process (GP) models are a class of flexible non-parametric mode...

2 Xiaoyu Lu, et al. ∙

research

∙ 05/10/2021

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Deep Gaussian processes (DGPs) have struggled for relevance in applicati...

23 Vincent Dutordoir, et al. ∙

research

∙ 04/12/2021

GPflux: A Library for Deep Gaussian Processes

We introduce GPflux, a Python library for Bayesian deep learning with a ...

28 Vincent Dutordoir, et al. ∙

research

∙ 06/30/2020

Sparse Gaussian Processes with Spherical Harmonic Features

We introduce a new class of inter-domain variational Gaussian processes ...

17 Vincent Dutordoir, et al. ∙

research

∙ 03/09/2020

Amortized variance reduction for doubly stochastic objectives

Approximate inference in complex probabilistic models such as deep Gauss...

3 Ayman Boustati, et al. ∙

research

∙ 03/02/2020

A Framework for Interdomain and Multioutput Gaussian Processes

One obstacle to the use of Gaussian processes (GPs) in large-scale probl...

11 Mark van der Wilk, et al. ∙

research

∙ 01/15/2020

Doubly Sparse Variational Gaussian Processes

The use of Gaussian process models is typically limited to datasets with...

27 Vincent Adam, et al. ∙

research

∙ 06/13/2019

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

We identify a new variational inference scheme for dynamical systems who...

3 Alessandro Davide Ialongo, et al. ∙

research

∙ 05/14/2019

Deep Gaussian Processes with Importance-Weighted Variational Inference

Deep Gaussian processes (DGPs) can model complex marginal densities as w...

12 Hugh Salimbeni, et al. ∙

research

∙ 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...

10 Nicolas Durrande, et al. ∙

research

∙ 02/15/2019

Translation Insensitivity for Deep Convolutional Gaussian Processes

Deep learning has been at the foundation of large improvements in image ...

18 Vincent Dutordoir, et al. ∙

research

∙ 12/14/2018

Non-Factorised Variational Inference in Dynamical Systems

We focus on variational inference in dynamical systems where the discret...

4 Alessandro Davide Ialongo, et al. ∙

research

∙ 11/15/2018

Infinite-Horizon Gaussian Processes

Gaussian processes provide a flexible framework for forecasting, removin...

8 Arno Solin, et al. ∙

research

∙ 10/30/2018

Gaussian Process Conditional Density Estimation

Conditional Density Estimation (CDE) models deal with estimating conditi...

2 Vincent Dutordoir, et al. ∙

research

∙ 08/16/2018

Learning Invariances using the Marginal Likelihood

Generalising well in supervised learning tasks relies on correctly extra...

0 Mark van der Wilk, et al. ∙

research

∙ 04/03/2018

Large-Scale Cox Process Inference using Variational Fourier Features

Gaussian process modulated Poisson processes provide a flexible framewor...

0 S. T. John, et al. ∙

research

∙ 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...

0 Hugh Salimbeni, et al. ∙

research

∙ 09/06/2017

Convolutional Gaussian Processes

We present a practical way of introducing convolutional structure into G...

0 Mark van der Wilk, et al. ∙

research

∙ 08/17/2017

Pseudo-extended Markov chain Monte Carlo

Sampling from the posterior distribution using Markov chain Monte Carlo ...

0 Christopher Nemeth, et al. ∙

research

∙ 08/16/2017

Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction

Missing data and noisy observations pose significant challenges for reli...

0 Hossein Soleimani, et al. ∙

research

∙ 05/30/2017

Identification of Gaussian Process State Space Models

The Gaussian process state space model (GPSSM) is a non-linear dynamical...

0 Stefanos Eleftheriadis, et al. ∙

research

∙ 11/21/2016

Variational Fourier features for Gaussian processes

This work brings together two powerful concepts in Gaussian processes: t...

0 James Hensman, et al. ∙

research

∙ 10/27/2016

GPflow: A Gaussian process library using TensorFlow

GPflow is a Gaussian process library that uses TensorFlow for its core c...

0 Alexander G. de G. Matthews, et al. ∙

research

∙ 04/18/2016

Chained Gaussian Processes

Gaussian process models are flexible, Bayesian non-parametric approaches...

0 Alan D. Saul, et al. ∙

research

∙ 06/12/2015

MCMC for Variationally Sparse Gaussian Processes

Gaussian process (GP) models form a core part of probabilistic machine l...

0 James Hensman, et al. ∙

research

∙ 05/10/2015

Spike and Slab Gaussian Process Latent Variable Models

The Gaussian process latent variable model (GP-LVM) is a popular approac...

0 Zhenwen Dai, et al. ∙

research

∙ 04/27/2015

On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes

The variational framework for learning inducing variables (Titsias, 2009...

0 Alexander G. de G. Matthews, et al. ∙

research

∙ 12/03/2014

Nested Variational Compression in Deep Gaussian Processes

Deep Gaussian processes provide a flexible approach to probabilistic mod...

0 James Hensman, et al. ∙

research

∙ 11/07/2014

Scalable Variational Gaussian Process Classification

Gaussian process classification is a popular method with a number of app...

0 James Hensman, et al. ∙

research

∙ 10/18/2014

Gaussian Process Models with Parallelization and GPU acceleration

In this work, we present an extension of Gaussian process (GP) models wi...

0 Zhenwen Dai, et al. ∙

research

∙ 01/08/2014

Fast nonparametric clustering of structured time-series

In this publication, we combine two Bayesian non-parametric models: the ...

0 James Hensman, et al. ∙

research

∙ 09/26/2013

Gaussian Processes for Big Data

We introduce stochastic variational inference for Gaussian process model...

0 James Hensman, et al. ∙

research

∙ 06/22/2012

Fast Variational Inference in the Conjugate Exponential Family

We present a general method for deriving collapsed variational inference...

0 James Hensman, et al. ∙

James Hensman

Featured Co-authors