Search Results for author: James Hensman

Found 36 papers, 13 papers with code

Sparse Gaussian Processes with Spherical Harmonic Features Revisited

no code implementations28 Mar 2023 Stefanos Eleftheriadis, Dominic Richards, James Hensman

Further, we introduce sparseness in the eigenbasis by variational learning of the spherical harmonic phases.

Gaussian Processes

Additive Gaussian Processes Revisited

1 code implementation20 Jun 2022 Xiaoyu Lu, Alexis Boukouvalas, James Hensman

Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power.

Gaussian Processes

Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes

no code implementations pproximateinference AABI Symposium 2022 Mark van der Wilk, Artem Artemev, James Hensman

The need for matrix decompositions (inverses) is often named as a major impediment to scaling Gaussian process (GP) models, even in efficient approximations.

Gaussian Processes

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

no code implementations NeurIPS 2021 Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande

This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy.

Bayesian Inference Gaussian Processes +1

Sparse Gaussian Processes with Spherical Harmonic Features

no code implementations ICML 2020 Vincent Dutordoir, Nicolas Durrande, James Hensman

We introduce a new class of inter-domain variational Gaussian processes (GP) where data is mapped onto the unit hypersphere in order to use spherical harmonic representations.

Gaussian Processes

Amortized variance reduction for doubly stochastic objectives

no code implementations9 Mar 2020 Ayman Boustati, Sattar Vakili, James Hensman, ST John

Approximate inference in complex probabilistic models such as deep Gaussian processes requires the optimisation of doubly stochastic objective functions.

Gaussian Processes Test

A Framework for Interdomain and Multioutput Gaussian Processes

1 code implementation2 Mar 2020 Mark van der Wilk, Vincent Dutordoir, ST John, Artem Artemev, Vincent Adam, James Hensman

One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for small variations in the model or inference.

Gaussian Processes

Doubly Sparse Variational Gaussian Processes

no code implementations15 Jan 2020 Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande, Artem Artemev, James Hensman

The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their complexity and memory footprint.

Gaussian Processes valid

Variational Gaussian Process Models without Matrix Inverses

no code implementations pproximateinference AABI Symposium 2019 Mark van der Wilk, ST John, Artem Artemev, James Hensman

We present a variational approximation for a wide range of GP models that does not require a matrix inverse to be performed at each optimisation step.

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

1 code implementation13 Jun 2019 Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

As we demonstrate in our experiments, the factorisation between latent system states and transition function can lead to a miscalibrated posterior and to learning unnecessarily large noise terms.

Variational Inference

Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era

no code implementations26 Feb 2019 Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman

Banded matrices can be used as precision matrices in several models including linear state-space models, some Gaussian processes, and Gaussian Markov random fields.

Gaussian Processes Variational Inference

Bayesian Image Classification with Deep Convolutional Gaussian Processes

no code implementations15 Feb 2019 Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman

We also demonstrate that our fully Bayesian approach improves on dropout-based Bayesian deep learning methods in terms of uncertainty and marginal likelihood estimates.

Classification Decision Making +5

Non-Factorised Variational Inference in Dynamical Systems

no code implementations14 Dec 2018 Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

We focus on variational inference in dynamical systems where the discrete time transition function (or evolution rule) is modelled by a Gaussian process.

Variational Inference

Infinite-Horizon Gaussian Processes

1 code implementation NeurIPS 2018 Arno Solin, James Hensman, Richard E. Turner

The complexity is still cubic in the state dimension $m$ which is an impediment to practical application.

Gaussian Processes

Learning Invariances using the Marginal Likelihood

no code implementations NeurIPS 2018 Mark van der Wilk, Matthias Bauer, ST John, James Hensman

Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space.

Data Augmentation Gaussian Processes +2

Large-Scale Cox Process Inference using Variational Fourier Features

no code implementations ICML 2018 S. T. John, James Hensman

This allows us to formulate a grid-free approximation that scales well with the number of data points and the size of the domain.

Small Data Image Classification

Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models

no code implementations24 Mar 2018 Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman

The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored.

Variational Inference

Convolutional Gaussian Processes

4 code implementations NeurIPS 2017 Mark van der Wilk, Carl Edward Rasmussen, James Hensman

We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images.

Gaussian Processes

Pseudo-extended Markov chain Monte Carlo

1 code implementation NeurIPS 2019 Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman

In this paper, we introduce the pseudo-extended MCMC method as a simple approach for improving the mixing of the MCMC sampler for multi-modal posterior distributions.

Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction

no code implementations16 Aug 2017 Hossein Soleimani, James Hensman, Suchi Saria

Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations.

Gaussian Processes Imputation +2

Identification of Gaussian Process State Space Models

no code implementations NeurIPS 2017 Stefanos Eleftheriadis, Thomas F. W. Nicholson, Marc Peter Deisenroth, James Hensman

To address this challenge, we impose a structured Gaussian variational posterior distribution over the latent states, which is parameterised by a recognition model in the form of a bi-directional recurrent neural network.

Variational Fourier features for Gaussian processes

1 code implementation21 Nov 2016 James Hensman, Nicolas Durrande, Arno Solin

This work brings together two powerful concepts in Gaussian processes: the variational approach to sparse approximation and the spectral representation of Gaussian processes.

Gaussian Processes

Chained Gaussian Processes

1 code implementation18 Apr 2016 Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

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

Additive models Gaussian Processes

MCMC for Variationally Sparse Gaussian Processes

no code implementations NeurIPS 2015 James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani

This paper simultaneously addresses these, using a variational approximation to the posterior which is sparse in support of the function but otherwise free-form.

Gaussian Processes

Spike and Slab Gaussian Process Latent Variable Models

no code implementations10 May 2015 Zhenwen Dai, James Hensman, Neil Lawrence

The Gaussian process latent variable model (GP-LVM) is a popular approach to non-linear probabilistic dimensionality reduction.

Dimensionality Reduction Gaussian Processes +2

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

no code implementations27 Apr 2015 Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani

We then discuss augmented index sets and show that, contrary to previous works, marginal consistency of augmentation is not enough to guarantee consistency of variational inference with the original model.

Variational Inference

Nested Variational Compression in Deep Gaussian Processes

no code implementations3 Dec 2014 James Hensman, Neil D. Lawrence

Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning.

Gaussian Processes Variational Inference

Scalable Variational Gaussian Process Classification

1 code implementation7 Nov 2014 James Hensman, Alex Matthews, Zoubin Ghahramani

Gaussian process classification is a popular method with a number of appealing properties.

Classification General Classification

Gaussian Process Models with Parallelization and GPU acceleration

no code implementations18 Oct 2014 Zhenwen Dai, Andreas Damianou, James Hensman, Neil Lawrence

In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration.

Fast nonparametric clustering of structured time-series

no code implementations8 Jan 2014 James Hensman, Magnus Rattray, Neil D. Lawrence

In this publication, we combine two Bayesian non-parametric models: the Gaussian Process (GP) and the Dirichlet Process (DP).

Clustering Nonparametric Clustering +3

Gaussian Processes for Big Data

8 code implementations26 Sep 2013 James Hensman, Nicolo Fusi, Neil D. Lawrence

We introduce stochastic variational inference for Gaussian process models.

Gaussian Processes Variational Inference

Fast Variational Inference in the Conjugate Exponential Family

no code implementations NeurIPS 2012 James Hensman, Magnus Rattray, Neil D. Lawrence

We present a general method for deriving collapsed variational inference algorithms for probabilistic models in the conjugate exponential family.

Variational Inference

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