Search Results for author:
Found 35 papers, 22 papers with code
Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing
However, much of this research has primarily focused on daytime satellite imagery due to the limitation that most pre-trained models are trained on 3-band RGB images.
Transformer Neural Processes -- Kernel Regression
Stochastic processes model various natural phenomena from disease transmission to stock prices, but simulating and quantifying their uncertainty can be computationally challenging.
Graph Agnostic Causal Bayesian Optimisation
To the best of our knowledge, our work is the first to study causal Bayesian optimization with cumulative regret objectives in scenarios where the graph is unknown or partially known.
KidSat: satellite imagery to map childhood poverty dataset and benchmark
Using our dataset we benchmark multiple models, from low-level satellite imagery models such as MOSAIKS , to deep learning foundation models, which include both generic vision models such as Self-Distillation with no Labels (DINOv2) models and specific satellite imagery models such as SatMAE.
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 in the fields of public health and disease surveillance.
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs).
City-Wide Perceptions of Neighbourhood Quality using Street View Images
Researchers demonstrated the efficacy of crowd-sourcing perception ratings of image pairs across 56 cities and training a model to predict perceptions from street-view images.
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Here we develop a new class of spatiotemporal Hawkes processes that can capture both triggering and clustering behavior and we provide an efficient method for performing inference.
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions.
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
Epidemic models are powerful tools in understanding infectious disease.
PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability
In this paper we introduce a new problem within the growing literature of interpretability for convolution neural networks (CNNs).
PriorVAE: Encoding spatial priors with VAEs for small-area estimation
Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling.
Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting
An important example of this problem is the nowcasting of COVID-19 mortality: given a stream of reported counts of daily deaths, can we correct for the delays in reporting to paint an accurate picture of the present, with uncertainty?
Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model Selection
The approach is shown to be useful in practice when applied to a real problem - to perform model selection for a semi-mechanistic hierarchical Bayesian model of COVID-19 transmission in South Korea involving the integration of a 200D density.
Improving axial resolution in SIM using deep learning
Structured Illumination Microscopy is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy.
A unified machine learning approach to time series forecasting applied to demand at emergency departments
We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE.
Bayesian Probabilistic Numerical Integration with Tree-Based Models
The advantages and disadvantages of this new methodology are highlighted on a set of benchmark tests including the Genz functions, and on a Bayesian survey design problem.
BART-based inference for Poisson processes
The effectiveness of Bayesian Additive Regression Trees (BART) has been demonstrated in a variety of contexts including non-parametric regression and classification.
Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
Our model estimates these changes by calculating backwards from temporal data on observed to estimate the number of infections and rate of transmission that occurred several weeks prior, allowing for a probabilistic time lag between infection and death.
Applications Methodology
$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions to enable statistical inference (such as the integral of a log Gaussian process).
Modeling and Forecasting Art Movements with CGANs
Realisations from this distribution can be used by the CGAN to generate "future" paintings.
Interpreting Deep Neural Networks Through Variable Importance
While the success of deep neural networks (DNNs) is well-established across a variety of domains, our ability to explain and interpret these methods is limited.
Multimodal Sentiment Analysis To Explore the Structure of Emotions
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing.
Variational Learning on Aggregate Outputs with Gaussian Processes
While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs.
Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"
For inference, we discretize the spatiotemporal point pattern and learn a log-intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods.
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features
The use of covariance kernels is ubiquitous in the field of spatial statistics.
Bayesian Approaches to Distribution Regression
Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level.
Detecting causal associations in large nonlinear time series datasets
Detecting causal associations in time series datasets is a key challenge for novel insights into complex dynamical systems such as the Earth system or the human brain.
Methodology Atmospheric and Oceanic Physics Applications
Is Gun Violence Contagious?
Existing theories of gun violence predict stable spatial concentrations and contagious diffusion of gun violence into surrounding areas.
Applications
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 outcomes from the 2016 US presidential election.
Poisson intensity estimation with reproducing kernels
However, we prove that the representer theorem does hold in an appropriately transformed RKHS, guaranteeing that the optimization of the penalized likelihood can be cast as a tractable finite-dimensional problem.
European Union regulations on algorithmic decision-making and a "right to explanation"
We summarize the potential impact that the European Union's new General Data Protection Regulation will have on the routine use of machine learning algorithms.
Collaborative Filtering with Side Information: a Gaussian Process Perspective
We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression.
Bayesian Learning of Kernel Embeddings
The posterior mean of our model is closely related to recently proposed shrinkage estimators for kernel mean embeddings, while the posterior uncertainty is a new, interesting feature with various possible applications.
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure.
