Search Results for author: Chris Holmes

Found 25 papers, 9 papers with code

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

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.

On Locality of Local Explanation Models

no code implementations 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).

Imputation

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 +3

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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