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07/11/2023
Differentially Private Statistical Inference through β-Divergence One Posterior Sampling
Differential privacy guarantees allow the results of a statistical analy...
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06/26/2023
Challenges and Opportunities of Shapley values in a Clinical Context
With the adoption of machine learning-based solutions in routine clinica...
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06/26/2023
PWSHAP: A Path-Wise Explanation Model for Targeted Variables
Predictive black-box models can exhibit high accuracy but their opaque n...
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06/09/2023
Semiparametric posterior corrections
We present a new approach to semiparametric inference using corrected po...
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05/31/2023
A Unified Framework for U-Net Design and Analysis
U-Nets are a go-to, state-of-the-art neural architecture across numerous...
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04/04/2023
Learning from data with structured missingness
Missing data are an unavoidable complication in many machine learning ta...
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01/31/2023
On the Stability of General Bayesian Inference
We study the stability of posterior predictive inferences to the specifi...
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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...
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01/17/2023
Causal Falsification of Digital Twins
Digital twins hold substantial promise in many applications, but rigorou...
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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...
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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...
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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...
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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...
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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...
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07/25/2022
Causal predictive inference and target trial emulation
Causal inference from observational data can be viewed as a missing data...
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06/18/2022
Bayesian Lesion Estimation with a Structured Spike-and-Slab Prior
Neural demyelination and brain damage accumulated in white matter appear...
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06/13/2022
Density Estimation with Autoregressive Bayesian Predictives
Bayesian methods are a popular choice for statistical inference in small...
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03/01/2022
Neural Score Matching for High-Dimensional Causal Inference
Traditional methods for matching in causal inference are impractical for...
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02/01/2022
A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples
Multiplexed immunofluorescence provides an unprecedented opportunity for...
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09/28/2021
Interoperability of statistical models in pandemic preparedness: principles and reality
We present "interoperability" as a guiding framework for statistical mod...
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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...
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06/24/2021
On Locality of Local Explanation Models
Shapley values provide model agnostic feature attributions for model out...
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06/11/2021
Conformal Bayesian Computation
We develop scalable methods for producing conformal Bayesian predictive ...
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03/29/2021
Martingale Posterior Distributions
The prior distribution on parameters of a likelihood is the usual starti...
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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...
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03/05/2021
Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness
We propose a variational autoencoder architecture to model both ignorabl...
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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 ...
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11/16/2020
Foundations of Bayesian Learning from Synthetic Data
There is significant growth and interest in the use of synthetic data as...
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07/14/2020
Explicit Regularisation in Gaussian Noise Injections
We study the regularisation induced in neural networks by Gaussian noise...
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07/14/2020
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
We make inroads into understanding the robustness of Variational Autoenc...
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07/14/2020
Relaxed-Responsibility Hierarchical Discrete VAEs
Successfully training Variational Autoencoders (VAEs) with a hierarchy o...
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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...
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06/15/2020
Neural Ensemble Search for Performant and Calibrated Predictions
Ensembles of neural networks achieve superior performance compared to st...
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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 ...
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02/18/2020
Learning Bijective Feature Maps for Linear ICA
Separating high-dimensional data like images into independent latent fac...
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09/25/2019
Regularising Deep Networks with DGMs
Here we develop a new method for regularising neural networks where we l...
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09/25/2019
Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders
In clustering we normally output one cluster variable for each datapoint...
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06/01/2019
Disentangling Improves VAEs' Robustness to Adversarial Attacks
This paper is concerned with the robustness of VAEs to adversarial attac...
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05/21/2019
On the marginal likelihood and cross-validation
In Bayesian statistics, the marginal likelihood, also known as the evide...
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02/08/2019
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Increasingly complex datasets pose a number of challenges for Bayesian i...
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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...
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12/06/2018
A Framework for Adaptive MCMC Targeting Multimodal Distributions
We propose a new Monte Carlo method for sampling from multimodal distrib...
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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...
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02/26/2018
Principles of Bayesian Inference using General Divergence Criteria
When it is acknowledged that all candidate parameterised statistical mod...
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02/26/2018
Principled Bayesian Minimum Divergence Inference
When it is acknowledged that all candidate parameterised statistical mod...
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09/22/2017
Generalized Bayesian Updating and the Loss-Likelihood Bootstrap
In this paper, we revisit the weighted likelihood bootstrap and show tha...
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05/11/2015