Chris Holmes

is this you? claim profile

∙ 0 followers
University of Oxford ∙ 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,

Explicit Regularisation in Gaussian Noise Injections

We study the regularisation induced in neural networks by Gaussian noise...
07/14/2020 ∙ by Alexander Camuto, et al. ∙ 0 ∙ share

read it
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders

We make inroads into understanding the robustness of Variational Autoenc...
07/14/2020 ∙ by Alexander Camuto, et al. ∙ 16 ∙ share

read it
Relaxed-Responsibility Hierarchical Discrete VAEs

Successfully training Variational Autoencoders (VAEs) with a hierarchy o...
07/14/2020 ∙ by Matthew Willetts, et al. ∙ 0 ∙ share

read it
Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers

The Covid-19 pandemic has resulted in a variety of approaches for managi...
07/09/2020 ∙ by Tom Lovett, et al. ∙ 0 ∙ share

read it
Neural Ensemble Search for Performant and Calibrated Predictions

Ensembles of neural networks achieve superior performance compared to st...
06/15/2020 ∙ by Sheheryar Zaidi, et al. ∙ 15 ∙ share

read it
Risk scoring calculation for the current NHSx contact tracing app

We consider how the NHS COVID-19 application will initially calculate a ...
05/22/2020 ∙ by Mark Briers, et al. ∙ 0 ∙ share

read it
Learning Bijective Feature Maps for Linear ICA

Separating high-dimensional data like images into independent latent fac...
02/18/2020 ∙ by Alexander Camuto, et al. ∙ 19 ∙ share

read it
Regularising Deep Networks with DGMs

Here we develop a new method for regularising neural networks where we l...
09/25/2019 ∙ by Matthew Willetts, et al. ∙ 10 ∙ share

read it
Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders

In clustering we normally output one cluster variable for each datapoint...
09/25/2019 ∙ by Matthew Willetts, et al. ∙ 4 ∙ share

read it
Disentangling Improves VAEs' Robustness to Adversarial Attacks

This paper is concerned with the robustness of VAEs to adversarial attac...
06/01/2019 ∙ by Matthew Willetts, et al. ∙ 0 ∙ share

read it
On the marginal likelihood and cross-validation

In Bayesian statistics, the marginal likelihood, also known as the evide...
05/21/2019 ∙ by Edwin Fong, et al. ∙ 6 ∙ share

read it
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap

Increasingly complex datasets pose a number of challenges for Bayesian i...
02/08/2019 ∙ by Edwin Fong, et al. ∙ 10 ∙ share

read it
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...
12/21/2018 ∙ by Sebastian Vollmer, et al. ∙ 0 ∙ share

read it
A Framework for Adaptive MCMC Targeting Multimodal Distributions

We propose a new Monte Carlo method for sampling from multimodal distrib...
12/06/2018 ∙ by Emilia Pompe, et al. ∙ 0 ∙ share

read it
Semi-unsupervised Learning of Human Activity using Deep Generative Models

Here we demonstrate a new deep generative model for classification. We i...
10/29/2018 ∙ by Matthew Willetts, et al. ∙ 0 ∙ share

read it
Principles of Bayesian Inference using General Divergence Criteria

When it is acknowledged that all candidate parameterised statistical mod...
02/26/2018 ∙ by Jack Jewson, et al. ∙ 0 ∙ share

read it
Principled Bayesian Minimum Divergence Inference

When it is acknowledged that all candidate parameterised statistical mod...
02/26/2018 ∙ by Jack Jewson, et al. ∙ 0 ∙ share

read it
Generalized Bayesian Updating and the Loss-Likelihood Bootstrap

In this paper, we revisit the weighted likelihood bootstrap and show tha...
09/22/2017 ∙ by Simon Lyddon, et al. ∙ 0 ∙ share

read it
On Markov chain Monte Carlo methods for tall data

Markov chain Monte Carlo methods are often deemed too computationally in...
05/11/2015 ∙ by Rémi Bardenet, et al. ∙ 0 ∙ share

read it

Chris Holmes

Featured Co-authors

Yee Whye Teh

Stephen Roberts

Frank Hutter

Arnaud Doucet

Umut Şimşekli

Tom Rainforth

Jim Q. Smith

Franz J Király

Brooks Paige

Matthew Willetts

Rayid Ghani

Explicit Regularisation in Gaussian Noise Injections

Towards a Theoretical Understanding of the Robustness of Variational Autoencoders

Relaxed-Responsibility Hierarchical Discrete VAEs

Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers

Neural Ensemble Search for Performant and Calibrated Predictions

Risk scoring calculation for the current NHSx contact tracing app

Learning Bijective Feature Maps for Linear ICA

Regularising Deep Networks with DGMs

Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders

Disentangling Improves VAEs' Robustness to Adversarial Attacks

On the marginal likelihood and cross-validation

Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap

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

A Framework for Adaptive MCMC Targeting Multimodal Distributions

Semi-unsupervised Learning of Human Activity using Deep Generative Models

Principles of Bayesian Inference using General Divergence Criteria

Principled Bayesian Minimum Divergence Inference

Generalized Bayesian Updating and the Loss-Likelihood Bootstrap

On Markov chain Monte Carlo methods for tall data