b'Chris Holmes'

research

∙ 01/31/2023

On the Stability of General Bayesian Inference

We study the stability of posterior predictive inferences to the specifi...

Jack Jewson, et al. ∙

research

∙ 01/19/2023

A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs

U-Net architectures are ubiquitous in state-of-the-art deep learning, ho...

Fabian Falck, et al. ∙

research

∙ 01/17/2023

Causal Falsification of Digital Twins

Digital twins hold substantial promise in many applications, but rigorou...

Rob Cornish, et al. ∙

research

∙ 12/15/2022

Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers

Recent work has reported that AI classifiers trained on audio recordings...

Harry Coppock, et al. ∙

research

∙ 12/15/2022

Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19

Since early in the coronavirus disease 2019 (COVID-19) pandemic, there h...

Davide Pigoli, et al. ∙

research

∙ 12/15/2022

A large-scale and PCR-referenced vocal audio dataset for COVID-19

The UK COVID-19 Vocal Audio Dataset is designed for the training and eva...

Jobie Budd, et al. ∙

research

∙ 10/27/2022

Generating the right evidence at the right time: Principles of a new class of flexible augmented clinical trial designs

The past few years have seen an increasing number of initiatives aimed a...

Cornelia Dunger-Baldauf, et al. ∙

research

∙ 10/11/2022

Assessments and developments in constructing a National Health Index for policy making, in the United Kingdom

Composite indicators are a useful tool to summarize, measure and compare...

Anna Freni-Sterrantino, et al. ∙

research

∙ 07/25/2022

Causal predictive inference and target trial emulation

Causal inference from observational data can be viewed as a missing data...

Andrew Yiu, et al. ∙

research

∙ 06/18/2022

Bayesian Lesion Estimation with a Structured Spike-and-Slab Prior

Neural demyelination and brain damage accumulated in white matter appear...

Anna Menacher, et al. ∙

research

∙ 06/13/2022

Density Estimation with Autoregressive Bayesian Predictives

Bayesian methods are a popular choice for statistical inference in small...

Sahra Ghalebikesabi, et al. ∙

research

∙ 03/01/2022

Neural Score Matching for High-Dimensional Causal Inference

Traditional methods for matching in causal inference are impractical for...

Oscar Clivio, et al. ∙

research

∙ 02/01/2022

A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples

Multiplexed immunofluorescence provides an unprecedented opportunity for...

Natalia Garcia Martin, et al. ∙

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

Featured Co-authors