A Temporal Model of Linear Anti-Hebbian Learning. - Mark A. Girolami; Colin Fyfe (Neural Process. Lett., 1996)

Multivariate Density Factorization for Independent Component Analysis: An Unsupervised Artificial Neural Network Approach. - Mark Girolami; Colin Fyfe (AISTATS, 1997)

Independence is far from normal. - Mark A. Girolami; Colin Fyfe (ESANN, 1997)

Principal Components Identify MLP Hidden Layer Size for Optimal Generalisation Performance. - Mark Girolami (ICANNGA, 1997)

Fahlman-Type Activation Functions Applied to Nonlinear PCA Networks Provide a Generalised Independent Component Analysis. - Mark Girolami; Colin Fyfe (ICANNGA, 1997)

Kurtosis extrema and identification of independent components: a neural network approach. - Mark A. Girolami; Colin Fyfe (ICASSP, 1997)

Generalised independent component analysis through unsupervised learning with emergent Bussgang properties. - Mark A. Girolami; Colin Fyfe (ICNN, 1997)

Stochastic ICA Contrast Maximisation Using Oja's Nonlinear PCA Algorithm. - Mark A. Girolami; Colin Fyfe (Int. J. Neural Syst., 1997)

An extended exploratory projection pursuit network with linear and nonlinear anti-hebbian lateral connections applied to the cocktail party problem. - Mark A. Girolami; Colin Fyfe (Neural Networks, 1997)

Noise reduction and speech enhancement via temporal anti-Hebbian learning. - Mark A. Girolami (ICASSP, 1998)

A nonlinear model of the binaural cocktail party effect. - Mark A. Girolami (Neurocomputing, 1998)

An Alternative Perspective on Adaptive Independent Component Analysis Algorithms. - Mark A. Girolami (Neural Comput., 1998)

The Latent Variable Data Model for Exploratory Data Analysis and Visualisation: A Generalisation of the Nonlinear Infomax Algorithm. - Mark A. Girolami (Neural Process. Lett., 1998)

A common neural-network model for unsupervised exploratory data analysis and independent component analysis. - Mark A. Girolami; Andrzej Cichocki; Shun-ichi Amari (IEEE Trans. Neural Networks, 1998)

Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources. - Te-Won Lee; Mark A. Girolami; Terrence J. Sejnowski (Neural Comput., 1999)

Blind source separation of more sources than mixtures using overcomplete representations. - Te-Won Lee; Michael S. Lewicki; Mark A. Girolami; Terrence J. Sejnowski (IEEE Signal Process. Lett., 1999)

Extraction of Sleep-Spindles from the Electroencephalogram (EEG). - Allan Kardec Barros; Roman Rosipal; Mark A. Girolami; Georg Dorffner; Noboru Ohnishi (ANNIMAB, 2000)

Kernel PCA Feature Extraction of Event-Related Potentials for Human Signal Detection Performance. - Roman Rosipal; Mark A. Girolami; Leonard J. Trejo (ANNIMAB, 2000)

The Organization and Visualization of Document Corpora: A Probabilistic Approach. - Mark A. Girolami; Alexei Vinokourov; Ata Kabán (DEXA Workshop, 2000)

A generative model for sparse discrete binary data with non-uniform categorical priors. - Mark A. Girolami (ESANN, 2000)

Initialized and Guided EM-Clustering of Sparse Binary Data with Application to Text Based Documents. - Ata Kabán; Mark A. Girolami (ICPR, 2000)

Probabilistic Hierarchical Clustering Method for Organizing Collections of Text Documents. - Alexei Vinokourov; Mark A. Girolami (ICPR, 2000)

Finding Topics in Dynamical Text: Application to Chat Line Discussions. - Ella Bingham; Ata Kabán; Mark A. Girolami (WWW Posters, 2001)

Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression. - Roman Rosipal; Mark A. Girolami; Leonard J. Trejo; Andrzej Cichocki (Neural Comput. Appl., 2001)

A Variational Method for Learning Sparse and Overcomplete Representations. - Mark A. Girolami (Neural Comput., 2001)

An Expectation-Maximization Approach to Nonlinear Component Analysis. - Roman Rosipal; Mark A. Girolami (Neural Comput., 2001)

A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data. - Ata Kabán; Mark A. Girolami (IEEE Trans. Pattern Anal. Mach. Intell., 2001)

The topographic organization and visualization of binary data using multivariate-Bernoulli latent variable models. - Mark A. Girolami (IEEE Trans. Neural Networks, 2001)

A General Framework for a Principled Hierarchical Visualization of Multivariate Data. - Ata Kabán; Peter Tiño; Mark A. Girolami (IDEAL, 2002)

Latent variable models for the topographic organisation of discrete and strictly positive data. - Mark A. Girolami (Neurocomputing, 2002)

A Dynamic Probabilistic Model to Visualise Topic Evolution in Text Streams. - Ata Kabán; Mark A. Girolami (J. Intell. Inf. Syst., 2002)

A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections. - Alexei Vinokourov; Mark A. Girolami (J. Intell. Inf. Syst., 2002)

Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem. - Mark A. Girolami (Neural Comput., 2002)

Fast Extraction of Semantic Features from a Latent Semantic Indexed Text Corpus. - Ata Kabán; Mark A. Girolami (Neural Process. Lett., 2002)

Report on the 24th European colloquium on information retrieval research (ECIR 2002). - Fabio Crestani; Mark A. Girolami; C. J. van Rijsbergen (SIGIR Forum, 2002)

Report on the 24th European Colloquium on Information Retrieval Research. - Fabio Crestani; Mark A. Girolami (SIGMOD Rec., 2002)

Mercer kernel-based clustering in feature space. - Mark A. Girolami (IEEE Trans. Neural Networks, 2002)

Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles. - Mark A. Girolami; Ata Kabán (NIPS, 2003)

Investigating the relationship between language model perplexity and IR precision-recall measures. - Leif Azzopardi; Mark A. Girolami; Keith van Rijsbergen (SIGIR, 2003)

On an equivalence between PLSI and LDA. - Mark A. Girolami; Ata Kabán (SIGIR, 2003)

Topic Identification in Dynamical Text by Complexity Pursuit. - Ella Bingham; Ata Kabán; Mark A. Girolami (Neural Process. Lett., 2003)

Probability Density Estimation from Optimally Condensed Data Samples. - Mark A. Girolami; Chao He (IEEE Trans. Pattern Anal. Mach. Intell., 2003)

An Assessment of Feature Relevance in Predicting Protein Function from Sequence. - Ali Al-Shahib; Chao He; Aik Choon Tan; Mark A. Girolami; David R. Gilbert (IDEAL, 2004)

Topic based language models for ad hoc information retrieval. - Leif Azzopardi; Mark Girolami; C. J. van Rijsbergen (IJCNN, 2004)

User biased document language modelling. - Leif Azzopardi; Mark A. Girolami; Cornelis Joost van Rijsbergen (SIGIR, 2004)

Biologically valid linear factor models of gene expression. - Mark A. Girolami; Rainer Breitling (Bioinform., 2004)

Employing optimized combinations of one-class classifiers for automated currency validation. - Chao He; Mark A. Girolami; Gary Ross (Pattern Recognit., 2004)

Novelty detection employing an L2 optimal non-parametric density estimator. - Chao He; Mark A. Girolami (Pattern Recognit. Lett., 2004)

Disease Classification from Capillary Electrophoresis: Mass Spectrometry. - Simon Rogers; Mark A. Girolami; Ronald Krebs; Harald Mischak (ICAPR (1), 2005)

Hierarchic Bayesian models for kernel learning. - Mark A. Girolami; Simon Rogers (ICML, 2005)

Probabilistic hyperspace analogue to language. - Leif Azzopardi; Mark A. Girolami; Malcolm K. Crowe (SIGIR, 2005)

A Bayesian regression approach to the inference of regulatory networks from gene expression data. - Simon Rogers; Mark A. Girolami (Bioinform., 2005)

Sequential Activity Profiling: Latent Dirichlet Allocation of Markov Chains. - Mark A. Girolami; Ata Kabán (Data Min. Knowl. Discov., 2005)

The Latent Process Decomposition of cDNA Microarray Data Sets. - Simon Rogers; Mark A. Girolami; Colin Campbell; Rainer Breitling (IEEE ACM Trans. Comput. Biol. Bioinform., 2005)

Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation. - Gavin C. Cawley; Nicola L. C. Talbot; Mark A. Girolami (NIPS, 2006)

Data Integration for Classification Problems Employing Gaussian Process Priors. - Mark A. Girolami; Mingjun Zhong (NIPS, 2006)

Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm. - Robert Jenssen; Torbjørn Eltoft; Mark A. Girolami; Deniz Erdogmus (NIPS, 2006)

Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. - Mark A. Girolami; Simon Rogers (Neural Comput., 2006)

Clustering via kernel decomposition. - Anna Szymkowiak-Have; Mark A. Girolami; Jan Larsen (IEEE Trans. Neural Networks, 2006)

Detecting worm variants using machine learning. - Oliver Sharma; Mark A. Girolami; Joseph S. Sventek (CoNEXT, 2007)

Bayesian model-based inference of transcription factor activity. - Simon Rogers; Raya Khanin; Mark A. Girolami (BMC Bioinform., 2007)

Multi-class Semi-supervised Learning with the e-truncated Multinomial Probit Gaussian Process. - Simon Rogers; Mark A. Girolami (Gaussian Processes in Practice, 2007)

An empirical analysis of the probabilistic K-nearest neighbour classifier. - S. Manocha; Mark A. Girolami (Pattern Recognit. Lett., 2007)

Employing Latent Dirichlet Allocation for fraud detection in telecommunications. - Dongshan Xing; Mark A. Girolami (Pattern Recognit. Lett., 2007)

Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins. - Theodoros Damoulas; Yiming Ying; Mark A. Girolami; Colin Campbell (ICMLA, 2008)

Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes. - Ben Calderhead; Mark A. Girolami; Neil D. Lawrence (NIPS, 2008)

Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection. - Theodoros Damoulas; Mark A. Girolami (Bioinform., 2008)

vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R. - Nicola Lama; Mark A. Girolami (Bioinform., 2008)

ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction. - Ian M. Overton; Gianandrea Padovani; Mark A. Girolami; Geoffrey J. Barton (Bioinform., 2008)

Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models. - Simon Rogers; Mark A. Girolami; Walter Kolch; Katrina M. Waters; Tao Liu 003; Brian Thrall; H. Steven Wiley (Bioinform., 2008)

Bayesian ranking of biochemical system models. - Vladislav Vyshemirsky; Mark A. Girolami (Bioinform., 2008)

BioBayes: A software package for Bayesian inference in systems biology. - Vladislav Vyshemirsky; Mark A. Girolami (Bioinform., 2008)

Classifying EEG for brain computer interfaces using Gaussian processes. - Mingjun Zhong; Fabien Lotte; Mark A. Girolami; Anatole Lécuyer (Pattern Recognit. Lett., 2008)

Bayesian inference for differential equations. - Mark A. Girolami (Theor. Comput. Sci., 2008)

Analysis of SVM with Indefinite Kernels. - Yiming Ying; Colin Campbell; Mark A. Girolami (NIPS, 2009)

Using Higher-Order Dynamic Bayesian Networks to Model Periodic Data from the Circadian Clock of Arabidopsis Thaliana. - Rónán Daly; Kieron D. Edwards; John S. O'Neill; J. Stuart Aitken; Andrew J. Millar; Mark A. Girolami (PRIB, 2009)

Definition of Valid Proteomic Biomarkers: A Bayesian Solution. - Keith Harris; Mark A. Girolami; Harald Mischak (PRIB, 2009)

Inferring Meta-covariates in Classification. - Keith Harris; Lisa McMillan; Mark A. Girolami (PRIB, 2009)

Semi-supervised Prediction of Protein Interaction Sentences Exploiting Semantically Encoded Metrics. - Tamara Polajnar; Mark A. Girolami (PRIB, 2009)

Classification of Protein Interaction Sentences via Gaussian Processes. - Tamara Polajnar; Simon Rogers; Mark A. Girolami (PRIB, 2009)

Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm. - Yiming Ying; Colin Campbell; Theodoros Damoulas; Mark A. Girolami (PRIB, 2009)

Probabilistic assignment of formulas to mass peaks in metabolomics experiments. - Simon Rogers; Richard A. Scheltema; Mark A. Girolami; Rainer Breitling (Bioinform., 2009)

Estimating Bayes factors via thermodynamic integration and population MCMC. - Ben Calderhead; Mark A. Girolami (Comput. Stat. Data Anal., 2009)

Reversible Jump MCMC for Non-Negative Matrix Factorization. - Mingjun Zhong; Mark A. Girolami (AISTATS, 2009)

Combining feature spaces for classification. - Theodoros Damoulas; Mark A. Girolami (Pattern Recognit., 2009)

Pattern recognition with a Bayesian kernel combination machine. - Theodoros Damoulas; Mark A. Girolami (Pattern Recognit. Lett., 2009)

System Identification and Model Ranking: The Bayesian Perspective. - Mark Girolami; Ben Calderhead; Vladislav Vyshemirsky (Learning and Inference in Computational Systems Biology, 2010)

Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers. - Mohammed Dakna; Keith Harris; Alexandros Kalousis; Sebastien Carpentier; Walter Kolch; Joost P. Schanstra; Marion Haubitz; Antonia Vlahou; Harald Mischak; Mark A. Girolami (BMC Bioinform., 2010)

Infinite factorization of multiple non-parametric views. - Simon Rogers; Arto Klami; Janne Sinkkonen; Mark A. Girolami; Samuel Kaski (Mach. Learn., 2010)

Semi-parametric analysis of multi-rater data. - Simon Rogers; Mark A. Girolami; Tamara Polajnar (Stat. Comput., 2010)

Multiclass relevance vector machines: sparsity and accuracy. - Ioannis Psorakis; Theodoros Damoulas; Mark A. Girolami (IEEE Trans. Neural Networks, 2010)

A First Course in Machine Learning. - Simon Rogers; Mark A. Girolami (CRC Press, 2011)

On the Use of Diagonal and Class-Dependent Weighted Distances for the Probabilistic k-Nearest Neighbor. - Roberto Paredes; Mark A. Girolami (IbPRIA, 2011)

Protein interaction sentence detection using multiple semantic kernels. - Tamara Polajnar; Theodoros Damoulas; Mark A. Girolami (J. Biomed. Semant., 2011)

Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation. - Tamara Polajnar; Simon Rogers; Mark A. Girolami (Int. J. Data Min. Bioinform., 2011)

ITFoM - The IT Future of Medicine. - Hans Lehrach; Ralf Sudbrak; Peter Boyle; Markus Pasterk; Kurt Zatloukal; Heimo Müller; Tim Hubbard; Angela Brand; Mark A. Girolami; Daniel Jameson; Frank J. Bruggeman; Hans V. Westerhoff (FET, 2011)

Discussion of the paper: "Sampling schemes for generalized linear Dirichlet process random effects models" by M. Kyung, J. Gill, and G. Casella. - Maurizio Filippone; Antonietta Mira; Mark A. Girolami (Stat. Methods Appl., 2011)

A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices. - Mingjun Zhong; Mark A. Girolami (ICML, 2012)

Preface. - Neil D. Lawrence; Mark A. Girolami (AISTATS, 2012)

Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations. - Ke Yuan; Mark A. Girolami; Mahesan Niranjan (Neural Comput., 2012)

Analysing user behaviour through dynamic population models. - Matthew Higgs; Alistair Morrison; Mark A. Girolami; Matthew Chalmers (CHI Extended Abstracts, 2013)

Exact-Approximate Bayesian Inference for Gaussian Processes. - Maurizio Filippone; Mark A. Girolami (CoRR, 2013)

A comparative evaluation of stochastic-based inference methods for Gaussian process models. - Maurizio Filippone; Mingjun Zhong; Mark A. Girolami (Mach. Learn., 2013)

Online Learning with (Multiple) Kernels: A Review. - Tom Diethe; Mark A. Girolami (Neural Comput., 2013)

Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel. - Vassilios Stathopoulos 003; Veronica Zamora-Gutierrez; Kate E. Jones; Mark A. Girolami (AISTATS, 2014)

Putting the Scientist in the Loop - Accelerating Scientific Progress with Interactive Machine Learning. - Oisin Mac Aodha; Vassilios Stathopoulos 003; Gabriel J. Brostow; Michael Terry; Mark A. Girolami; Kate E. Jones (ICPR, 2014)

Probabilistic Model Checking of DTMC Models of User Activity Patterns. - Oana Andrei; Muffy Calder; Matthew Higgs; Mark A. Girolami (QEST, 2014)

mcmc_clib-an advanced MCMC sampling package for ode models. - Andrei Kramer; Vassilios Stathopoulos 003; Mark A. Girolami; Nicole Radde (Bioinform., 2014)

Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions. - Samuel Livingstone; Mark A. Girolami (Entropy, 2014)

Pseudo-Marginal Bayesian Inference for Gaussian Processes. - Maurizio Filippone; Mark A. Girolami (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Ordinal Mixed Membership Models. - Seppo Virtanen; Mark A. Girolami (ICML, 2015)

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne (NIPS, 2015)

Probabilistic Integration. - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne; Dino Sejdinovic (CoRR, 2015)

Probabilistic Numerics and Uncertainty in Computations. - Philipp Hennig; Michael A. Osborne; Mark A. Girolami (CoRR, 2015)

Unbiased Bayes for Big Data: Paths of Partial Posteriors. - Heiko Strathmann; Dino Sejdinovic; Mark A. Girolami (CoRR, 2015)

Control Functionals for Quasi-Monte Carlo Integration. - Chris J. Oates; Mark A. Girolami (AISTATS, 2016)

Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems. - Jon Cockayne; Chris J. Oates; Tim Sullivan 001; Mark A. Girolami (CoRR, 2016)

A Bayesian approach to multiscale inverse problems with on-the-fly scale determination. - Louis Ellam; Nicholas Zabaras; Mark A. Girolami (J. Comput. Phys., 2016)

Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian Inverse Problems. - Shiwei Lan; Tan Bui-Thanh; Mike Christie; Mark A. Girolami (J. Comput. Phys., 2016)

On the Sampling Problem for Kernel Quadrature. - François-Xavier Briol; Chris J. Oates; Jon Cockayne; Wilson Ye Chen; Mark A. Girolami (ICML, 2017)

Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models. - Chris J. Oates; Steven A. Niederer; Angela W. C. Lee; François-Xavier Briol; Mark A. Girolami (NIPS, 2017)

Geometry and Dynamics for Markov Chain Monte Carlo. - Alessandro Barp; François-Xavier Briol; Anthony D. Kennedy; Mark A. Girolami (CoRR, 2017)

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. - Jon Cockayne; Chris J. Oates; Tim Sullivan 001; Mark A. Girolami (CoRR, 2017)

Geometric MCMC for infinite-dimensional inverse problems. - Alexandros Beskos; Mark A. Girolami; Shiwei Lan; Patrick E. Farrell; Andrew M. Stuart (J. Comput. Phys., 2017)

Statistical analysis of differential equations: introducing probability measures on numerical solutions. - Patrick R. Conrad; Mark A. Girolami; Simo Särkkä; Andrew M. Stuart; Konstantinos Zygalakis (Stat. Comput., 2017)

Bayesian Quadrature for Multiple Related Integrals. - Xiaoyue Xi; François-Xavier Briol; Mark A. Girolami (ICML, 2018)

A Bayesian Conjugate Gradient Method. - Jon Cockayne; Chris J. Oates; Mark A. Girolami (CoRR, 2018)

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne; Dino Sejdinovic (CoRR, 2018)

Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success. - Richard Scalzo; David Kohn; Hugo K. H. Olierook; Gregory Houseman; Rohitash Chandra; Mark A. Girolami; Sally Cripps (CoRR, 2018)

How Deep Are Deep Gaussian Processes? - Matthew M. Dunlop; Mark A. Girolami; Andrew M. Stuart; Aretha L. Teckentrup (J. Mach. Learn. Res., 2018)

Bat detective - Deep learning tools for bat acoustic signal detection. - Oisin Mac Aodha; Rory Gibb; Kate E. Barlow; Ella Browning; Michael Firman; Robin Freeman; Briana Harder; Libby Kinsey; Gary R. Mead; Stuart E. Newson; Ivan Pandourski; Stuart Parsons; et al. (PLoS Comput. Biol., 2018)

Stein Point Markov Chain Monte Carlo. - Wilson Ye Chen; Alessandro Barp; François-Xavier Briol; Jackson Gorham; Mark A. Girolami; Lester W. Mackey; Chris J. Oates (ICML, 2019)

Minimum Stein Discrepancy Estimators. - Alessandro Barp; François-Xavier Briol; Andrew B. Duncan; Mark A. Girolami; Lester W. Mackey (NeurIPS, 2019)

Multi-resolution Multi-task Gaussian Processes. - Oliver Hamelijnck; Theodoros Damoulas; Kangrui Wang; Mark A. Girolami (NeurIPS, 2019)

Precision-Recall Balanced Topic Modelling. - Seppo Virtanen; Mark A. Girolami (NeurIPS, 2019)

A Methodology for Prognostics Under the Conditions of Limited Failure Data Availability. - Gishan Don Ranasinghe; Tony Lindgren; Mark Girolami; Ajith Kumar Parlikad (IEEE Access, 2019)

Statistical Inference for Generative Models with Maximum Mean Discrepancy. - François-Xavier Briol; Alessandro Barp; Andrew B. Duncan; Mark A. Girolami (CoRR, 2019)

Embedded Ridge Approximations: Constructing Ridge Approximations Over Localized Scalar Fields For Improved Simulation-Centric Dimension Reduction. - Chun Yui Wong; Pranay Seshadri; Geoffrey T. Parks; Mark A. Girolami (CoRR, 2019)

The synthesis of data from instrumented structures and physics-based models via Gaussian processes. - Alastair Gregory; F. Din-Houn Lau; Mark A. Girolami; Liam J. Butler; Mohammed Z. E. B. Elshafie (J. Comput. Phys., 2019)

Editorial: special edition on probabilistic numerics. - Mark A. Girolami; Ilse C. F. Ipsen; Chris J. Oates; Art B. Owen; Timothy John Sullivan (Stat. Comput., 2019)

Bayesian Probabilistic Numerical Methods. - Jon Cockayne; Chris J. Oates; Timothy John Sullivan; Mark A. Girolami (SIAM Rev., 2019)

Dynamic content based ranking. - Seppo Virtanen; Mark Girolami (AISTATS, 2020)

Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models. - George Wynne; François-Xavier Briol; Mark A. Girolami (CoRR, 2020)

Continuous calibration of a digital twin: comparison of particle filter and Bayesian calibration approaches. - Rebecca Ward; Ruchi Choudhary; Alastair Gregory; Mark Girolami (CoRR, 2020)

Bayesian Assessments of Aeroengine Performance. - Pranay Seshadri; Andrew B. Duncan; George Thorne; Geoffrey T. Parks; Mark Girolami (CoRR, 2020)

Posterior inference for sparse hierarchical non-stationary models. - Karla Monterrubio-Gómez; Lassi Roininen; Sara Wade; Theodoros Damoulas; Mark Girolami (Comput. Stat. Data Anal., 2020)

A Self-Sensing Digital Twin of a Railway Bridge using the Statistical Finite Element Method. - Eky Febrianto; Liam J. Butler; Mark Girolami; Fehmi Cirak (CoRR, 2021)

Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction. - Andrea Marinoni; Saloua Chlaily; Eduard Khachatrian; Torbjørn Eltoft; Sivasakthy Selvakumaran; Mark Girolami; Christian Jutten (CoRR, 2021)

PeriPy - A High Performance OpenCL Peridynamics Package. - Benjamin Boys; Tim J. Dodwell; Mark Hobbs; Mark Girolami (CoRR, 2021)

A graph representation based on fluid diffusion model for multimodal data analysis: theoretical aspects and enhanced community detection. - Andrea Marinoni; Christian Jutten; Mark Girolami (CoRR, 2021)

Uncertainty quantification for data-driven turbulence modelling with Mondrian forests. - Ashley Scillitoe; Pranay Seshadri; Mark Girolami (J. Comput. Phys., 2021)

Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness. - George Wynne; François-Xavier Briol; Mark Girolami (J. Mach. Learn. Res., 2021)

Integration in reproducing kernel Hilbert spaces of Gaussian kernels. - Toni Karvonen; Chris J. Oates; Mark Girolami (Math. Comput., 2021)

Scaling digital twins from the artisanal to the industrial. - Steven A. Niederer; Michael S. Sacks; Mark Girolami; Karen Willcox (Nat. Comput. Sci., 2021)

Lagrangian manifold Monte Carlo on Monge patches. - Marcelo Hartmann; Mark Girolami; Arto Klami (AISTATS, 2022)

Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents. - Alessandro Barp; Lancelot Da Costa; Guilherme França; Karl J. Friston; Mark A. Girolami; Michael I. Jordan; Grigorios A. Pavliotis (CoRR, 2022)

Knowledge Transfer in Engineering Fleets: Hierarchical Bayesian Modelling for Multi-Task Learning. - Lawrence A. Bull; Maharshi Harshadbhai Dhada; Olof Steinert; Tony Lindgren; Ajith Kumar Parlikad; Andrew B. Duncan; Mark Girolami (CoRR, 2022)

Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs. - Arnaud Vadeboncoeur; Ömer Deniz Akyildiz; Ieva Kazlauskaite; Mark Girolami; Fehmi Cirak (CoRR, 2022)

Targeted Separation and Convergence with Kernel Discrepancies. - Alessandro Barp; Carl-Johann Simon-Gabriel; Mark Girolami; Lester Mackey (CoRR, 2022)

Neural parameter calibration for large-scale multi-agent models. - Thomas Gaskin; Grigorios A. Pavliotis; Mark Girolami (CoRR, 2022)

$Φ$-DVAE: Learning Physically Interpretable Representations with Nonlinear Filtering. - Alex Glyn-Davies; Connor Duffin; Ömer Deniz Akyildiz; Mark Girolami (CoRR, 2022)

Low-rank statistical finite elements for scalable model-data synthesis. - Connor Duffin; Edward Cripps; Thomas Stemler; Mark Girolami (J. Comput. Phys., 2022)

Statistical Finite Elements via Langevin Dynamics. - Ömer Deniz Akyildiz; Connor Duffin; Sotirios Sabanis; Mark Girolami (SIAM/ASA J. Uncertain. Quantification, 2022)

A mixture modeling approach for clustering log files with coreset and user feedback. - Justin Bunker; Kristal Curtis; Mark Girolami; Ram Sriharsha (Pattern Recognit. Lett., 2022)

Anomaly detection in streaming data with gaussian process based stochastic differential equations. - Alex Glyn-Davies; Mark Girolami (Pattern Recognit. Lett., 2022)

Incorporating Reliability in Graph Information Propagation by Fluid Dynamics Diffusion: A case of Multimodal Semisupervised Deep Learning. - Andrea Marinoni; Marine Mercier; Qian Shi 001; Sivasakthy Selvakumaran; Mark Girolami (ICASSP, 2023)

Random Grid Neural Processes for Parametric Partial Differential Equations. - Arnaud Vadeboncoeur; Ieva Kazlauskaite; Yanni Papandreou; Fehmi Cirak; Mark Girolami; Ömer Deniz Akyildiz (ICML, 2023)

Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning. - Lawrence A. Bull; Domenic Di Francesco; Maharshi Harshadbhai Dhada; Olof Steinert; Tony Lindgren; Ajith Kumar Parlikad; Andrew B. Duncan; Mark Girolami (Comput. Aided Civ. Infrastructure Eng., 2023)

Inferring networks from time series: a neural approach. - Thomas Gaskin; Grigorios A. Pavliotis; Mark Girolami (CoRR, 2023)

Encoding Domain Expertise into Multilevel Models for Source Location. - Lawrence A. Bull; Matthew R. Jones; Elizabeth J. Cross; Andrew B. Duncan; Mark Girolami (CoRR, 2023)

Warped geometric information on the optimisation of Euclidean functions. - Marcelo Hartmann; Bernardo Williams; Hanlin Yu; Mark Girolami; Alessandro Barp; Arto Klami (CoRR, 2023)

Improving embedding of graphs with missing data by soft manifolds. - Andrea Marinoni; Pietro Lio'; Alessandro Barp; Christian Jutten; Mark Girolami (CoRR, 2023)

Bayesian learning via neural Schrödinger-Föllmer flows. - Francisco Vargas 001; Andrius Ovsianas; David Fernandes; Mark Girolami; Neil D. Lawrence; Nikolas Nüsken (Stat. Comput., 2023)

Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. - Simon Hubbert; Emilio Porcu; Chris J. Oates; Mark Girolami (Trans. Mach. Learn. Res., 2023)

Fully probabilistic deep models for forward and inverse problems in parametric PDEs. - Arnaud Vadeboncoeur; Ömer Deniz Akyildiz; Ieva Kazlauskaite; Mark Girolami; Fehmi Cirak (J. Comput. Phys., 2023)

Theoretical Guarantees for the Statistical Finite Element Method. - Yanni Papandreou; Jon Cockayne; Mark Girolami; Andrew B. Duncan (SIAM/ASA J. Uncertain. Quantification, 2023)

Riemannian Laplace Approximation with the Fisher Metric. - Hanlin Yu; Marcelo Hartmann; Bernardo Williams Moreno Sanchez; Mark Girolami; Arto Klami (AISTATS, 2024)

Generating Origin-Destination Matrices in Neural Spatial Interaction Models. - Ioannis Zachos; Mark Girolami; Theodoros Damoulas (NeurIPS, 2024)

Autoencoders in Function Space. - Justin Bunker; Mark Girolami; Hefin Lambley; Andrew M. Stuart; Tim Sullivan 001 (CoRR, 2024)

A Primer on Variational Inference for Physics-Informed Deep Generative Modelling. - Alex Glyn-Davies; Arnaud Vadeboncoeur; Ömer Deniz Akyildiz; Ieva Kazlauskaite; Mark Girolami (CoRR, 2024)

Bayesian dynamic modelling for probabilistic prediction of pavement condition. - Yiming Zhang; Alix Marie d'Avigneau; Georgios M. Hadjidemetriou; Lavindra de Silva; Mark Girolami; Ioannis K. Brilakis (Eng. Appl. Artif. Intell., 2024)

Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation. - Alex Glyn-Davies; Connor Duffin; Ömer Deniz Akyildiz; Mark Girolami (J. Comput. Phys., 2024)

The Bayesian Approach to Inverse Robin Problems. - Aksel K. Rasmussen; Fanny Seizilles; Mark Girolami; Ieva Kazlauskaite (SIAM/ASA J. Uncertain. Quantification, 2024)

Generative broad Bayesian (GBB) imputer for missing data imputation with uncertainty quantification. - Sin-Chi Kuok; Ka-Veng Yuen; Tim J. Dodwell; Mark Girolami (Knowl. Based Syst., 2024)

Tweedie Moment Projected Diffusions for Inverse Problems. - Benjamin Boys; Mark Girolami; Jakiw Pidstrigach; Sebastian Reich; Alan Mosca; Ömer Deniz Akyildiz (Trans. Mach. Learn. Res., 2024)

Near Real-Time Social Distance Estimation In London. - James Walsh 004; Oluwafunmilola Kesa; Andrew Wang 004; Mihai Ilas; Patrick O'Hara; Oscar Giles; Neil Dhir; Mark Girolami; Theodoros Damoulas (Comput. J., 2024)

Statistical Finite Elements via Interacting Particle Langevin Dynamics. - Alex Glyn-Davies; Connor Duffin; Ieva Kazlauskaite; Mark Girolami; Ömer Deniz Akyildiz (SIAM/ASA J. Uncertain. Quantification, 2025)

Error analysis for a statistical finite element method. - Toni Karvonen; Fehmi Cirak; Mark Girolami (J. Multivar. Anal., 2025)

Physics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests. - Daniel Camacho-Gómez; Carlos Borau; José Manuel García-Aznar; María José Gómez-Benito; Mark Girolami; María Ángeles Pérez 002 (npj Digit. Medicine, 2025)

Collaborative prognosis using a Weibull statistical hierarchical model. - Maharshi Harshadbhai Dhada; Lawrence A. Bull; Mark Girolami; Ajith Kumar Parlikad (Reliab. Eng. Syst. Saf., 2025)

Efficient Prior Calibration from Indirect Data. - Ömer Deniz Akyildiz; Mark Girolami; Andrew M. Stuart; Arnaud Vadeboncoeur (SIAM J. Sci. Comput., 2025)

Probabilistic Super-Resolution for High-Fidelity Physical System Simulations with Uncertainty Quantification. - Pengyu Zhang; Connor Duffin; Alex Glyn-Davies; Arnaud Vadeboncoeur; Mark Girolami (CoRR, 2025)

Efficient Deconvolution in Populational Inverse Problems. - Arnaud Vadeboncoeur; Mark Girolami; Andrew M. Stuart (CoRR, 2025)

Retrieval-augmented reasoning with lean language models. - Ryan Sze-Yin Chan; Federico Nanni; Tomas Lazauskas; Rosie Wood; Penelope Yong; Lionel Tarassenko; Mark Girolami; James Geddes; Andrew B. Duncan (CoRR, 2025)

Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later. - Arnaud Vadeboncoeur; Gregory Duthé; Mark Girolami; Eleni N. Chatzi (CoRR, 2025)