
Simulating normalising constants with referenced thermodynamic integration: application to COVID19 model selection
Model selection is a fundamental part of Bayesian statistical inference;...
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Improving axial resolution in SIM using deep learning
Structured Illumination Microscopy is a widespread methodology to image ...
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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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On the derivation of the renewal equation from an agedependent branching process: an epidemic modelling perspective
Renewal processes are a popular approach used in modelling infectious di...
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Bayesian Probabilistic Numerical Integration with TreeBased Models
Bayesian quadrature (BQ) is a method for solving numerical integration p...
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BARTbased inference for Poisson processes
The effectiveness of Bayesian Additive Regression Trees (BART) has been ...
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Estimating the number of infections and the impact of nonpharmaceutical interventions on COVID19 in European countries: technical description update
Following the emergence of a novel coronavirus (SARSCoV2) and its spre...
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πVAE: Encoding stochastic process priors with variational autoencoders
Stochastic processes provide a mathematically elegant way model complex ...
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Bayesian Kernel TwoSample Testing
In modern data analysis, nonparametric measures of discrepancies between...
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Modeling and Forecasting Art Movements with CGANs
Conditional Generative Adversarial Networks (CGANs) are a recent and pop...
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Interpreting Deep Neural Networks Through Variable Importance
While the success of deep neural networks (DNNs) is wellestablished acr...
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Multimodal Sentiment Analysis To Explore the Structure of Emotions
We propose a novel approach to multimodal sentiment analysis using deep ...
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Variational Learning on Aggregate Outputs with Gaussian Processes
While a typical supervised learning framework assumes that the inputs an...
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Scalable highresolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "RealTime Crime Forecasting Challenge"
This article describes Team Kernel Glitches' solution to the National In...
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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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Bayesian Approaches to Distribution Regression
Distribution regression has recently attracted much interest as a generi...
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Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata
We combine finegrained spatially referenced census data with the vote o...
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Poisson intensity estimation with reproducing kernels
Despite the fundamental nature of the inhomogeneous Poisson process in t...
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European Union regulations on algorithmic decisionmaking and a "right to explanation"
We summarize the potential impact that the European Union's new General ...
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Collaborative Filtering with Side Information: a Gaussian Process Perspective
We tackle the problem of collaborative filtering (CF) with side informat...
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Bayesian Learning of Kernel Embeddings
Kernel methods are one of the mainstays of machine learning, but the pro...
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Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
We present a scalable Gaussian process model for identifying and charact...
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Seth Flaxman
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