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05/31/2023
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya
Model-based disease mapping remains a fundamental policy-informing tool ...
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04/09/2023
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
In applied fields where the speed of inference and model flexibility are...
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11/22/2022
City-Wide Perceptions of Neighbourhood Quality using Street View Images
The interactions of individuals with city neighbourhoods is determined, ...
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10/21/2022
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Hawkes processes are point process models that have been used to capture...
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10/14/2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
As Gaussian processes mature, they are increasingly being deployed as pa...
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09/20/2022
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
Epidemic models are powerful tools in understanding infectious disease. ...
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12/31/2021
PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability
In this paper we introduce a new problem within the growing literature o...
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12/22/2021
COVID-19-Associated Orphanhood and Caregiver Death in the United States
Background: Most COVID-19 deaths occur among adults, not children, and a...
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10/24/2021
Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes
This article introduces epidemia, an R package for Bayesian, regression-...
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10/20/2021
Encoding spatiotemporal priors with VAEs for small-area estimation
Gaussian processes (GPs), implemented through multivariate Gaussian dist...
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06/23/2021
Regularised B-splines projected Gaussian Process priors to estimate the age profile of COVID-19 deaths before and after vaccine roll-out
The COVID-19 pandemic has caused severe public health consequences in th...
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06/10/2021
Are We There Yet? Big Data Significantly Overestimates COVID-19 Vaccination in the US
Public health efforts to control the COVID-19 pandemic rely on accurate ...
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05/04/2021
Multi-resolution Spatial Regression for Aggregated Data with an Application to Crop Yield Prediction
We develop a new methodology for spatial regression of aggregated output...
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02/22/2021
Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting
Updating observations of a signal due to the delays in the measurement p...
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12/01/2020
Semi-Mechanistic Bayesian Modeling of COVID-19 with Renewal Processes
We propose a general Bayesian approach to modeling epidemics such as COV...
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09/08/2020
Simulating normalising constants with referenced thermodynamic integration: application to COVID-19 model selection
Model selection is a fundamental part of Bayesian statistical inference;...
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09/04/2020
Improving axial resolution in SIM using deep learning
Structured Illumination Microscopy is a widespread methodology to image ...
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07/13/2020
A unified machine learning approach to time series forecasting applied to demand at emergency departments
There were 25.6 million attendances at Emergency Departments (EDs) in En...
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06/30/2020
On the derivation of the renewal equation from an age-dependent branching process: an epidemic modelling perspective
Renewal processes are a popular approach used in modelling infectious di...
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06/09/2020
Bayesian Probabilistic Numerical Integration with Tree-Based Models
Bayesian quadrature (BQ) is a method for solving numerical integration p...
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05/16/2020
BART-based inference for Poisson processes
The effectiveness of Bayesian Additive Regression Trees (BART) has been ...
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04/23/2020
Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
Following the emergence of a novel coronavirus (SARS-CoV-2) and its spre...
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02/17/2020
Ï€VAE: Encoding stochastic process priors with variational autoencoders
Stochastic processes provide a mathematically elegant way model complex ...
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02/13/2020
Bayesian Kernel Two-Sample Testing
In modern data analysis, nonparametric measures of discrepancies between...
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06/21/2019
Modeling and Forecasting Art Movements with CGANs
Conditional Generative Adversarial Networks (CGANs) are a recent and pop...
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01/28/2019
Interpreting Deep Neural Networks Through Variable Importance
While the success of deep neural networks (DNNs) is well-established acr...
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05/25/2018
Multimodal Sentiment Analysis To Explore the Structure of Emotions
We propose a novel approach to multimodal sentiment analysis using deep ...
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05/22/2018
Variational Learning on Aggregate Outputs with Gaussian Processes
While a typical supervised learning framework assumes that the inputs an...
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01/09/2018
Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"
This article describes Team Kernel Glitches' solution to the National In...
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11/15/2017
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features
The use of covariance kernels is ubiquitous in the field of spatial stat...
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05/11/2017
Bayesian Approaches to Distribution Regression
Distribution regression has recently attracted much interest as a generi...
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11/11/2016
Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata
We combine fine-grained spatially referenced census data with the vote o...
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10/27/2016
Poisson intensity estimation with reproducing kernels
Despite the fundamental nature of the inhomogeneous Poisson process in t...
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06/28/2016
European Union regulations on algorithmic decision-making and a "right to explanation"
We summarize the potential impact that the European Union's new General ...
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05/23/2016
Collaborative Filtering with Side Information: a Gaussian Process Perspective
We tackle the problem of collaborative filtering (CF) with side informat...
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03/07/2016
Bayesian Learning of Kernel Embeddings
Kernel methods are one of the mainstays of machine learning, but the pro...
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11/13/2015
