Search Results for author:
Found 36 papers, 16 papers with code
Targeting Relative Risk Heterogeneity with Causal Forests
Treatment effect heterogeneity (TEH), or variability in treatment effect for different subgroups within a population, is of significant interest in clinical trial analysis.
Differentially Private Statistical Inference through $β$-Divergence One Posterior Sampling
To ameliorate this, we propose $\beta$D-Bayes, a posterior sampling scheme from a generalised posterior targeting the minimisation of the $\beta$-divergence between the model and the data generating process.
PWSHAP: A Path-Wise Explanation Model for Targeted Variables
We introduce Path-Wise Shapley effects (PWSHAP), a framework for assessing the targeted effect of a binary (e. g.~treatment) variable from a complex outcome model.
A Unified Framework for U-Net Design and Analysis
U-Nets are a go-to, state-of-the-art neural architecture across numerous tasks for continuous signals on a square such as images and Partial Differential Equations (PDE), however their design and architecture is understudied.
Learning from data with structured missingness
Missing data are an unavoidable complication in many machine learning tasks.
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied.
Causal Falsification of Digital Twins
Digital twins hold substantial promise in many applications, but rigorous procedures for assessing their accuracy are essential for their widespread deployment in safety-critical settings.
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 has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings.
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 can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status.
A large-scale and PCR-referenced vocal audio dataset for COVID-19
The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date.
Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates
In the context of density estimation, the standard nonparametric Bayesian approach is to target the posterior predictive of the Dirichlet process mixture model.
Neural Score Matching for High-Dimensional Causal Inference
We leverage these balancing scores to perform matching for high-dimensional causal inference and call this procedure neural score matching.
A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples
Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions.
Mitigating Statistical Bias within Differentially Private Synthetic Data
Increasing interest in privacy-preserving machine learning has led to new and evolved approaches for generating private synthetic data from undisclosed real data.
On Locality of Local Explanation Models
Empirically, we observe that Neighbourhood Shapley values identify meaningful sparse feature relevance attributions that provide insight into local model behaviour, complimenting conventional Shapley analysis.
Multi-Facet Clustering Variational Autoencoders
Work in deep clustering focuses on finding a single partition of data.
Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness
We propose a variational autoencoder architecture to model both ignorable and nonignorable missing data using pattern-set mixtures as proposed by Little (1993).
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections
In this paper we focus on the so-called `implicit effect' of GNIs, which is the effect of the injected noise on the dynamics of SGD.
Foundations of Bayesian Learning from Synthetic Data
There is significant growth and interest in the use of synthetic data as an enabler for machine learning in environments where the release of real data is restricted due to privacy or availability constraints.
Relaxed-Responsibility Hierarchical Discrete VAEs
Successfully training Variational Autoencoders (VAEs) with a hierarchy of discrete latent variables remains an area of active research.
Explicit Regularisation in Gaussian Noise Injections
We study the regularisation induced in neural networks by Gaussian noise injections (GNIs).
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
We make inroads into understanding the robustness of Variational Autoencoders (VAEs) to adversarial attacks and other input perturbations.
Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers
The Covid-19 pandemic has resulted in a variety of approaches for managing infection outbreaks in international populations.
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
On a variety of classification tasks and modern architecture search spaces, we show that the resulting ensembles outperform deep ensembles not only in terms of accuracy but also uncertainty calibration and robustness to dataset shift.
Learning Bijective Feature Maps for Linear ICA
Separating high-dimensional data like images into independent latent factors, i. e independent component analysis (ICA), remains an open research problem.
Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders
In clustering we normally output one cluster variable for each datapoint.
Disentangling Improves VAEs' Robustness to Adversarial Attacks
This paper is concerned with the robustness of VAEs to adversarial attacks.
Regularising Deep Networks with Deep Generative Models
We develop a new method for regularising neural networks.
Improving VAEs' Robustness to Adversarial Attack
We make significant advances in addressing this issue by introducing methods for producing adversarially robust VAEs.
On the marginal likelihood and cross-validation
In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior.
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Increasingly complex datasets pose a number of challenges for Bayesian inference.
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 statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit.
Semi-unsupervised Learning of Human Activity using Deep Generative Models
We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning where, in the training data, some classes are sparsely labelled and others entirely unlabelled.
Probabilistic Boolean Tensor Decomposition
Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules Boolean algebra.
General Bayesian Updating and the Loss-Likelihood Bootstrap
In this paper we revisit the weighted likelihood bootstrap, a method that generates samples from an approximate Bayesian posterior of a parametric model.
On Markov chain Monte Carlo methods for tall data
Finally, we have only been able so far to propose subsampling-based methods which display good performance in scenarios where the Bernstein-von Mises approximation of the target posterior distribution is excellent.
