Search Results for author: Chris Holmes

Found 36 papers, 16 papers with code

Targeting Relative Risk Heterogeneity with Causal Forests

1 code implementation26 Sep 2023 Vik Shirvaikar, Chris Holmes

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

no code implementations11 Jul 2023 Jack Jewson, Sahra Ghalebikesabi, Chris Holmes

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

1 code implementation26 Jun 2023 Lucile Ter-Minassian, Oscar Clivio, Karla Diaz-Ordaz, Robin J. Evans, Chris Holmes

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.

Decision Making Explainable Artificial Intelligence (XAI)

A Unified Framework for U-Net Design and Analysis

1 code implementation31 May 2023 Christopher Williams, Fabian Falck, George Deligiannidis, Chris Holmes, Arnaud Doucet, Saifuddin Syed

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.

Image Segmentation Semantic Segmentation

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

no code implementations19 Jan 2023 Fabian Falck, Christopher Williams, Dominic Danks, George Deligiannidis, Christopher Yau, Chris Holmes, Arnaud Doucet, Matthew Willetts

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

1 code implementation17 Jan 2023 Rob Cornish, Muhammad Faaiz Taufiq, Arnaud Doucet, Chris Holmes

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.

Causal Inference

Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates

no code implementations13 Jun 2022 Sahra Ghalebikesabi, Chris Holmes, Edwin Fong, Brieuc Lehmann

In the context of density estimation, the standard nonparametric Bayesian approach is to target the posterior predictive of the Dirichlet process mixture model.

Density Estimation

Neural Score Matching for High-Dimensional Causal Inference

1 code implementation1 Mar 2022 Oscar Clivio, Fabian Falck, Brieuc Lehmann, George Deligiannidis, Chris Holmes

We leverage these balancing scores to perform matching for high-dimensional causal inference and call this procedure neural score matching.

Causal Inference Vocal Bursts Intensity Prediction

Mitigating Statistical Bias within Differentially Private Synthetic Data

no code implementations24 Aug 2021 Sahra Ghalebikesabi, Harrison Wilde, Jack Jewson, Arnaud Doucet, Sebastian Vollmer, Chris Holmes

Increasing interest in privacy-preserving machine learning has led to new and evolved approaches for generating private synthetic data from undisclosed real data.

Privacy Preserving

On Locality of Local Explanation Models

1 code implementation NeurIPS 2021 Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla Diaz-Ordaz, Chris Holmes

Empirically, we observe that Neighbourhood Shapley values identify meaningful sparse feature relevance attributions that provide insight into local model behaviour, complimenting conventional Shapley analysis.

Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness

1 code implementation5 Mar 2021 Sahra Ghalebikesabi, Rob Cornish, Luke J. Kelly, Chris Holmes

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

1 code implementation13 Feb 2021 Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris Holmes, Mert Gürbüzbalaban, Umut Şimşekli

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

no code implementations16 Nov 2020 Harrison Wilde, Jack Jewson, Sebastian Vollmer, Chris Holmes

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.

Synthetic Data Generation

Relaxed-Responsibility Hierarchical Discrete VAEs

no code implementations14 Jul 2020 Matthew Willetts, Xenia Miscouridou, Stephen Roberts, Chris Holmes

Successfully training Variational Autoencoders (VAEs) with a hierarchy of discrete latent variables remains an area of active research.

Towards a Theoretical Understanding of the Robustness of Variational Autoencoders

no code implementations14 Jul 2020 Alexander Camuto, Matthew Willetts, Stephen Roberts, Chris Holmes, Tom Rainforth

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

no code implementations9 Jul 2020 Tom Lovett, Mark Briers, Marcos Charalambides, Radka Jersakova, James Lomax, Chris Holmes

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

1 code implementation NeurIPS 2021 Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris Holmes, Frank Hutter, Yee Whye Teh

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.

Image Classification Neural Architecture Search

Learning Bijective Feature Maps for Linear ICA

no code implementations18 Feb 2020 Alexander Camuto, Matthew Willetts, Brooks Paige, Chris Holmes, Stephen Roberts

Separating high-dimensional data like images into independent latent factors, i. e independent component analysis (ICA), remains an open research problem.

Improving VAEs' Robustness to Adversarial Attack

no code implementations ICLR 2021 Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen Roberts, Chris Holmes

We make significant advances in addressing this issue by introducing methods for producing adversarially robust VAEs.

Adversarial Attack

On the marginal likelihood and cross-validation

no code implementations21 May 2019 Edwin Fong, Chris Holmes

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.

Semi-unsupervised Learning of Human Activity using Deep Generative Models

1 code implementation29 Oct 2018 Matthew Willetts, Aiden Doherty, Stephen Roberts, Chris Holmes

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.

Classification General Classification +4

Probabilistic Boolean Tensor Decomposition

1 code implementation ICML 2018 Tammo Rukat, Chris Holmes, Christopher Yau

Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules Boolean algebra.

Model Selection Tensor Decomposition

General Bayesian Updating and the Loss-Likelihood Bootstrap

no code implementations22 Sep 2017 Simon Lyddon, Chris Holmes, Stephen Walker

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

1 code implementation11 May 2015 Rémi Bardenet, Arnaud Doucet, Chris Holmes

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.

Bayesian Inference

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