Chris Holmes

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University of Oxford

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The Alan Turing Institute

Professor of Biostatistics in Genomics in the Nuffield Department of Clinical Medicine and the Department of Statistics at the University of Oxford, Fellowship of St Anne's College, Oxford, Professor of Biostatistics and a Fellow of Lincoln College,

research

∙ 09/28/2021

Interoperability of statistical models in pandemic preparedness: principles and reality

We present "interoperability" as a guiding framework for statistical mod...

George Nicholson, et al. ∙

research

∙ 08/24/2021

Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale

Increasing interest in privacy-preserving machine learning has led to ne...

Sahra Ghalebikesabi, et al. ∙

research

∙ 06/24/2021

On Locality of Local Explanation Models

Shapley values provide model agnostic feature attributions for model out...

Sahra Ghalebikesabi, et al. ∙

research

∙ 06/11/2021

Conformal Bayesian Computation

We develop scalable methods for producing conformal Bayesian predictive ...

Edwin Fong, et al. ∙

research

∙ 03/29/2021

Martingale Posterior Distributions

The prior distribution on parameters of a likelihood is the usual starti...

Edwin Fong, et al. ∙

research

∙ 03/23/2021

Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag

Obtaining up to date information on the number of UK COVID-19 regional i...

Radka Jersakova, et al. ∙

research

∙ 03/05/2021

Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness

We propose a variational autoencoder architecture to model both ignorabl...

Sahra Ghalebikesabi, et al. ∙

research

∙ 02/13/2021

Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections

Gaussian noise injections (GNIs) are a family of simple and widely-used ...

Alexander Camuto, et al. ∙

research

∙ 11/16/2020

Foundations of Bayesian Learning from Synthetic Data

There is significant growth and interest in the use of synthetic data as...

Harrison Wilde, et al. ∙

research

∙ 07/14/2020

Explicit Regularisation in Gaussian Noise Injections

We study the regularisation induced in neural networks by Gaussian noise...

Alexander Camuto, et al. ∙

research

∙ 07/14/2020

Towards a Theoretical Understanding of the Robustness of Variational Autoencoders

We make inroads into understanding the robustness of Variational Autoenc...

Alexander Camuto, et al. ∙

research

∙ 07/14/2020

Relaxed-Responsibility Hierarchical Discrete VAEs

Successfully training Variational Autoencoders (VAEs) with a hierarchy o...

Matthew Willetts, et al. ∙

research

∙ 07/09/2020

Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers

The Covid-19 pandemic has resulted in a variety of approaches for managi...

Tom Lovett, et al. ∙

research

∙ 06/15/2020

Neural Ensemble Search for Performant and Calibrated Predictions

Ensembles of neural networks achieve superior performance compared to st...

Sheheryar Zaidi, et al. ∙

research

∙ 05/22/2020

Risk scoring calculation for the current NHSx contact tracing app

We consider how the NHS COVID-19 application will initially calculate a ...

Mark Briers, et al. ∙

research

∙ 02/18/2020

Learning Bijective Feature Maps for Linear ICA

Separating high-dimensional data like images into independent latent fac...

Alexander Camuto, et al. ∙

research

∙ 09/25/2019

Regularising Deep Networks with DGMs

Here we develop a new method for regularising neural networks where we l...

Matthew Willetts, et al. ∙

research

∙ 09/25/2019

Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders

In clustering we normally output one cluster variable for each datapoint...

Matthew Willetts, et al. ∙

research

∙ 06/01/2019

Disentangling Improves VAEs' Robustness to Adversarial Attacks

This paper is concerned with the robustness of VAEs to adversarial attac...

Matthew Willetts, et al. ∙

research

∙ 05/21/2019

On the marginal likelihood and cross-validation

In Bayesian statistics, the marginal likelihood, also known as the evide...

Edwin Fong, et al. ∙

research

∙ 02/08/2019

Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap

Increasingly complex datasets pose a number of challenges for Bayesian i...

Edwin Fong, et al. ∙

research

∙ 12/21/2018

Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness

Machine learning (ML), artificial intelligence (AI) and other modern sta...

Sebastian Vollmer, et al. ∙

research

∙ 12/06/2018

A Framework for Adaptive MCMC Targeting Multimodal Distributions

We propose a new Monte Carlo method for sampling from multimodal distrib...

Emilia Pompe, et al. ∙

research

∙ 10/29/2018

Semi-unsupervised Learning of Human Activity using Deep Generative Models

Here we demonstrate a new deep generative model for classification. We i...

Matthew Willetts, et al. ∙

research

∙ 02/26/2018

Principles of Bayesian Inference using General Divergence Criteria

When it is acknowledged that all candidate parameterised statistical mod...

Jack Jewson, et al. ∙

research

∙ 02/26/2018

Principled Bayesian Minimum Divergence Inference

When it is acknowledged that all candidate parameterised statistical mod...

Jack Jewson, et al. ∙

research

∙ 09/22/2017

Generalized Bayesian Updating and the Loss-Likelihood Bootstrap

In this paper, we revisit the weighted likelihood bootstrap and show tha...

Simon Lyddon, et al. ∙

research

∙ 05/11/2015

On Markov chain Monte Carlo methods for tall data

Markov chain Monte Carlo methods are often deemed too computationally in...

Rémi Bardenet, et al. ∙

Chris Holmes

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