Mark Girolami

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 50

Top Topics

Domain Expertise

Hierarchical Bayesian

Top Venues

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Publications

Hanlin Yu, Marcelo Hartmann, Bernardo Williams Moreno Sanchez, Mark Girolami, Arto Klami
Riemannian Laplace Approximation with the Fisher Metric. AISTATS (2024)
Ömer Deniz Akyildiz, Mark Girolami, Andrew M. Stuart, Arnaud Vadeboncoeur
Efficient Prior Calibration From Indirect Data. CoRR (2024)
Alex Glyn-Davies, Connor Duffin, Ömer Deniz Akyildiz, Mark Girolami
Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation. J. Comput. Phys. 515 (2024)
James Walsh, Oluwafunmilola Kesa, Andrew Wang, Mihai Ilas, Patrick O'Hara, Oscar Giles, Neil Dhir, Mark Girolami, Theodoros Damoulas
Near Real-Time Social Distance Estimation In London. Comput. J. 67 (1) (2024)
Yiming Zhang, Alix Marie d'Avigneau, Georgios M. Hadjidemetriou, Lavindra de Silva, Mark Girolami, Ioannis K. Brilakis
Bayesian dynamic modelling for probabilistic prediction of pavement condition. Eng. Appl. Artif. Intell. 133 (2024)
Sin-Chi Kuok, Ka-Veng Yuen, Tim J. Dodwell, Mark Girolami
Generative broad Bayesian (GBB) imputer for missing data imputation with uncertainty quantification. Knowl. Based Syst. 301 (2024)
Marcelo Hartmann, Bernardo Williams, Hanlin Yu, Mark Girolami, Alessandro Barp, Arto Klami
Warped geometric information on the optimisation of Euclidean functions. CoRR (2023)
Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz
Random Grid Neural Processes for Parametric Partial Differential Equations. CoRR (2023)
Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz
Random Grid Neural Processes for Parametric Partial Differential Equations. ICML (2023)
Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross, Andrew B. Duncan, Mark Girolami
Encoding Domain Expertise into Multilevel Models for Source Location. CoRR (2023)
Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Mark Girolami, Arto Klami
Riemannian Laplace Approximation with the Fisher Metric. CoRR (2023)
Thomas Gaskin, Grigorios A. Pavliotis, Mark Girolami
Inferring networks from time series: a neural approach. CoRR (2023)
Andrea Marinoni, Pietro Lio', Alessandro Barp, Christian Jutten, Mark Girolami
Improving embedding of graphs with missing data by soft manifolds. CoRR (2023)
Lawrence A. Bull, Domenic Di Francesco, Maharshi Harshadbhai Dhada, Olof Steinert, Tony Lindgren, Ajith Kumar Parlikad, Andrew B. Duncan, Mark Girolami
Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning. Comput. Aided Civ. Infrastructure Eng. 38 (7) (2023)
Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak
Fully probabilistic deep models for forward and inverse problems in parametric PDEs. J. Comput. Phys. 491 (2023)
Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. Trans. Mach. Learn. Res. 2023 (2023)
Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan
Theoretical Guarantees for the Statistical Finite Element Method. SIAM/ASA J. Uncertain. Quantification 11 (4) (2023)
Andrea Marinoni, Marine Mercier, Qian Shi, Sivasakthy Selvakumaran, Mark Girolami
Incorporating Reliability in Graph Information Propagation by Fluid Dynamics Diffusion: A case of Multimodal Semisupervised Deep Learning. ICASSP (2023)
Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
Bayesian learning via neural Schrödinger-Föllmer flows. Stat. Comput. 33 (1) (2023)
Alex Glyn-Davies, Connor Duffin, Ömer Deniz Akyildiz, Mark Girolami
$Φ$-DVAE: Learning Physically Interpretable Representations with Nonlinear Filtering. CoRR (2022)
Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak
Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs. CoRR (2022)
Ömer Deniz Akyildiz, Connor Duffin, Sotirios Sabanis, Mark Girolami
Statistical Finite Elements via Langevin Dynamics. SIAM/ASA J. Uncertain. Quantification 10 (4) (2022)
Toni Karvonen, Fehmi Cirak, Mark Girolami
Error analysis for a statistical finite element method. CoRR (2022)
Marcelo Hartmann, Mark Girolami, Arto Klami
Lagrangian Manifold Monte Carlo on Monge Patches. CoRR (2022)
Alex Glyn-Davies, Mark Girolami
Anomaly detection in streaming data with gaussian process based stochastic differential equations. Pattern Recognit. Lett. 153 (2022)
L. A. Bull, Maharshi Harshadbhai Dhada, Olof Steinert, Tony Lindgren, Ajith Kumar Parlikad, Andrew B. Duncan, Mark Girolami
Knowledge Transfer in Engineering Fleets: Hierarchical Bayesian Modelling for Multi-Task Learning. CoRR (2022)
Connor Duffin, Edward Cripps, Thomas Stemler, Mark Girolami
Low-rank statistical finite elements for scalable model-data synthesis. J. Comput. Phys. 463 (2022)
Justin Bunker, Kristal Curtis, Mark Girolami, Ram Sriharsha
A mixture modeling approach for clustering log files with coreset and user feedback. Pattern Recognit. Lett. 156 (2022)
Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. CoRR (2022)
Thomas Gaskin, Grigorios A. Pavliotis, Mark Girolami
Neural parameter calibration for large-scale multi-agent models. CoRR (2022)
Marcelo Hartmann, Mark Girolami, Arto Klami
Lagrangian manifold Monte Carlo on Monge patches. AISTATS (2022)
Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami, Lester Mackey
Targeted Separation and Convergence with Kernel Discrepancies. CoRR (2022)
Eky Febrianto, Liam J. Butler, Mark Girolami, Fehmi Cirak
A Self-Sensing Digital Twin of a Railway Bridge using the Statistical Finite Element Method. CoRR (2021)
George Wynne, François-Xavier Briol, Mark Girolami
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness. J. Mach. Learn. Res. 22 (2021)
Toni Karvonen, Chris J. Oates, Mark Girolami
Integration in reproducing kernel Hilbert spaces of Gaussian kernels. Math. Comput. 90 (331) (2021)
B. Boys, Tim J. Dodwell, M. Hobbs, Mark Girolami
PeriPy - A High Performance OpenCL Peridynamics Package. CoRR (2021)
Ömer Deniz Akyildiz, Connor Duffin, Sotirios Sabanis, Mark Girolami
Statistical Finite Elements via Langevin Dynamics. CoRR (2021)
Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan
Theoretical Guarantees for the Statistical Finite Element Method. CoRR (2021)
Andrea Marinoni, Saloua Chlaily, Eduard Khachatrian, Torbjørn Eltoft, Sivasakthy Selvakumaran, Mark Girolami, Christian Jutten
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction. CoRR (2021)
Steven A. Niederer, Michael S. Sacks, Mark Girolami, Karen Willcox
Scaling digital twins from the artisanal to the industrial. Nat. Comput. Sci. 1 (5) (2021)
Ashley Scillitoe, Pranay Seshadri, Mark Girolami
Uncertainty quantification for data-driven turbulence modelling with Mondrian forests. J. Comput. Phys. 430 (2021)
Andrea Marinoni, Christian Jutten, Mark Girolami
A graph representation based on fluid diffusion model for multimodal data analysis: theoretical aspects and enhanced community detection. CoRR (2021)
Connor Duffin, Edward Cripps, Thomas Stemler, Mark Girolami
Low-rank statistical finite elements for scalable model-data synthesis. CoRR (2021)
Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
Bayesian Learning via Neural Schrödinger-Föllmer Flows. CoRR (2021)
Karla Monterrubio-Gómez, Lassi Roininen, Sara Wade, Theodoros Damoulas, Mark Girolami
Posterior inference for sparse hierarchical non-stationary models. Comput. Stat. Data Anal. 148 (2020)
Pranay Seshadri, Andrew B. Duncan, George Thorne, Geoffrey T. Parks, Mark Girolami
Bayesian Assessments of Aeroengine Performance. CoRR (2020)
Toni Karvonen, Chris J. Oates, Mark Girolami
Integration in reproducing kernel Hilbert spaces of Gaussian kernels. CoRR (2020)
Seppo Virtanen, Mark Girolami
Dynamic content based ranking. AISTATS (2020)
Rebecca Ward, Ruchi Choudhary, Alastair Gregory, Mark Girolami
Continuous calibration of a digital twin: comparison of particle filter and Bayesian calibration approaches. CoRR (2020)
Gishan Don Ranasinghe, Tony Lindgren, Mark Girolami, Ajith Kumar Parlikad
A Methodology for Prognostics Under the Conditions of Limited Failure Data Availability. IEEE Access 7 (2019)
Mark Girolami, Colin Fyfe
Fahlman-Type Activation Functions Applied to Nonlinear PCA Networks Provide a Generalised Independent Component Analysis. ICANNGA (1997)
Mark Girolami, Colin Fyfe
Multivariate Density Factorization for Independent Component Analysis: An Unsupervised Artificial Neural Network Approach. AISTATS (1997)
Mark Girolami
Principal Components Identify MLP Hidden Layer Size for Optimal Generalisation Performance. ICANNGA (1997)