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08/16/2023
Warped geometric information on the optimisation of Euclidean functions
We consider the fundamental task of optimizing a real-valued function de...
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07/11/2023
Exploring Model Misspecification in Statistical Finite Elements via Shallow Water Equations
The abundance of observed data in recent years has increased the number ...
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07/05/2023
Table inference for combinatorial origin-destination choices in agent-based population synthesis
A key challenge in agent-based mobility simulations is the synthesis of ...
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05/15/2023
Encoding Domain Expertise into Multilevel Models for Source Location
Data from populations of systems are prevalent in many industrial applic...
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03/30/2023
Inferring networks from time series: a neural approach
Network structures underlie the dynamics of many complex phenomena, from...
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03/23/2023
Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation
We study a class of interacting particle systems for implementing a marg...
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01/26/2023
Random Grid Neural Processes for Parametric Partial Differential Equations
We introduce a new class of spatially stochastic physics and data inform...
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11/16/2022
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres
This work provides theoretical foundations for kernel methods in the hyp...
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09/30/2022
Φ-DVAE: Learning Physically Interpretable Representations with Nonlinear Filtering
Incorporating unstructured data into physical models is a challenging pr...
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09/27/2022
Neural parameter calibration for large-scale multi-agent models
Computational models have become a powerful tool in the quantitative sci...
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09/26/2022
Targeted Separation and Convergence with Kernel Discrepancies
Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD...
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08/09/2022
Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs
We formulate a class of physics-driven deep latent variable models (PDDL...
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03/20/2022
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents
In this chapter, we identify fundamental geometric structures that under...
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02/01/2022
Lagrangian Manifold Monte Carlo on Monge Patches
The efficiency of Markov Chain Monte Carlo (MCMC) depends on how the und...
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01/19/2022
Error analysis for a statistical finite element method
The recently proposed statistical finite element (statFEM) approach synt...
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12/07/2021
A graph representation based on fluid diffusion model for multimodal data analysis: theoretical aspects and enhanced community detection
Representing data by means of graph structures identifies one of the mos...
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11/20/2021
Bayesian Learning via Neural Schrödinger-Föllmer Flows
In this work we explore a new framework for approximate Bayesian inferen...
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11/15/2021
Theoretical Guarantees for the Statistical Finite Element Method
The statistical finite element method (StatFEM) is an emerging probabili...
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10/22/2021
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices
Inverse problems involving partial differential equations (PDEs) are wid...
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10/21/2021
Statistical Finite Elements via Langevin Dynamics
The recent statistical finite element method (statFEM) provides a cohere...
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09/10/2021
Low-rank statistical finite elements for scalable model-data synthesis
Statistical learning additions to physically derived mathematical models...
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07/23/2021
Optimization on manifolds: A symplectic approach
There has been great interest in using tools from dynamical systems and ...
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05/08/2021
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction
Modern data analytics take advantage of ensemble learning and transfer l...
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05/06/2021
A Unifying and Canonical Description of Measure-Preserving Diffusions
A complete recipe of measure-preserving diffusions in Euclidean space wa...
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03/25/2021
A Self-Sensing Digital Twin of a Railway Bridge using the Statistical Finite Element Method
The monitoring of infrastructure assets using sensor networks is becomin...
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11/30/2020
Bayesian Assessments of Aeroengine Performance
Aeroengine performance is determined by temperature and pressure profile...
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11/19/2020
Continuous calibration of a digital twin: comparison of particle filter and Bayesian calibration approaches
Assimilation of continuously streamed monitored data is an essential com...
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04/27/2020
Integration in reproducing kernel Hilbert spaces of Gaussian kernels
The Gaussian kernel plays a central role in machine learning, uncertaint...
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01/31/2020
Semi-Exact Control Functionals From Sard's Method
This paper focuses on the numerical computation of posterior expected qu...
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01/29/2020
Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models
Gaussian processes are ubiquitous in statistical analysis, machine learn...
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10/11/2019
Burglary in London: Insights from Statistical Heterogeneous Spatial Point Processes
To obtain operational insights regarding the crime of burglary in London...
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07/16/2019
Embedded Ridge Approximations: Constructing Ridge Approximations Over Localized Scalar Fields For Improved Simulation-Centric Dimension Reduction
Many quantities of interest (qois) arising from differential-equation-ce...
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06/19/2019
Multi-resolution Multi-task Gaussian Processes
We consider evidence integration from potentially dependent observation ...
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06/19/2019
Minimum Stein Discrepancy Estimators
When maximum likelihood estimation is infeasible, one often turns to sco...
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06/13/2019
Statistical Inference for Generative Models with Maximum Mean Discrepancy
While likelihood-based inference and its variants provide a statisticall...
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05/15/2019
The Statistical Finite Element Method
The finite element method (FEM) is one of the great triumphs of modern d...
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05/09/2019
Stein Point Markov Chain Monte Carlo
An important task in machine learning and statistics is the approximatio...
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03/07/2019
Hamiltonian Monte Carlo on Symmetric and Homogeneous Spaces via Symplectic Reduction
The Hamiltonian Monte Carlo method generates samples by introducing a me...
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01/14/2019
Optimality Criteria for Probabilistic Numerical Methods
It is well understood that Bayesian decision theory and average case ana...
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12/02/2018
Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success
The rigorous quantification of uncertainty in geophysical inversions is ...
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11/27/2018
The synthesis of data from instrumented structures and physics-based models via Gaussian processes
A recent development which is poised to disrupt current structural engin...
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11/26/2018
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
This article is the rejoinder for the paper "Probabilistic Integration: ...
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10/11/2018
A Riemannian-Stein Kernel Method
This paper presents a theoretical analysis of numerical integration base...
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10/08/2018
Probabilistic Linear Solvers: A Unifying View
Several recent works have developed a new, probabilistic interpretation ...
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04/04/2018
Posterior Inference for Sparse Hierarchical Non-stationary Models
Gaussian processes are valuable tools for non-parametric modelling, wher...
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01/16/2018
A Bayesian Conjugate Gradient Method
A fundamental task in numerical computation is the solution of large lin...
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01/12/2018
Bayesian Quadrature for Multiple Related Integrals
Bayesian probabilistic numerical methods are a set of tools providing po...
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12/05/2017
Posterior Integration on a Riemannian Manifold
The geodesic Markov chain Monte Carlo method and its variants enable com...
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11/30/2017
How Deep Are Deep Gaussian Processes?
Recent research has shown the potential utility of probability distribut...
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09/11/2017