Mark A. Girolami

ORCID

Publication Activity (10 Years)

Years Active: 1996-2022
Publications (10 Years): 37

Top Venues

Publications

Alessandro Barp, Lancelot Da Costa, Guilherme França, Karl J. Friston, Mark A. Girolami, Michael I. Jordan, Grigorios A. Pavliotis
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents. CoRR (2022)
George Wynne, François-Xavier Briol, Mark A. Girolami
Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models. CoRR (2020)
Jon Cockayne, Chris J. Oates, Timothy John Sullivan, Mark A. Girolami
Bayesian Probabilistic Numerical Methods. SIAM Rev. 61 (4) (2019)
Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates
Stein Point Markov Chain Monte Carlo. ICML (2019)
Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey
Minimum Stein Discrepancy Estimators. CoRR (2019)
Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark A. Girolami
Multi-resolution Multi-task Gaussian Processes. CoRR (2019)
Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark A. Girolami
Multi-resolution Multi-task Gaussian Processes. NeurIPS (2019)
Seppo Virtanen, Mark A. Girolami
Precision-Recall Balanced Topic Modelling. NeurIPS (2019)
Mark A. Girolami, Ilse C. F. Ipsen, Chris J. Oates, Art B. Owen, Timothy John Sullivan
Editorial: special edition on probabilistic numerics. Stat. Comput. 29 (6) (2019)
Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey
Minimum Stein Discrepancy Estimators. NeurIPS (2019)
François-Xavier Briol, Alessandro Barp, Andrew B. Duncan, Mark A. Girolami
Statistical Inference for Generative Models with Maximum Mean Discrepancy. CoRR (2019)
Alastair Gregory, F. Din-Houn Lau, Mark A. Girolami, Liam J. Butler, Mohammed Z. E. B. Elshafie
The synthesis of data from instrumented structures and physics-based models via Gaussian processes. J. Comput. Phys. 392 (2019)
Chun Yui Wong, Pranay Seshadri, Geoffrey T. Parks, Mark A. Girolami
Embedded Ridge Approximations: Constructing Ridge Approximations Over Localized Scalar Fields For Improved Simulation-Centric Dimension Reduction. CoRR (2019)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". CoRR (2018)
Xiaoyue Xi, François-Xavier Briol, Mark A. Girolami
Bayesian Quadrature for Multiple Related Integrals. CoRR (2018)
Jon Cockayne, Chris J. Oates, Mark A. Girolami
A Bayesian Conjugate Gradient Method. CoRR (2018)
Xiaoyue Xi, François-Xavier Briol, Mark A. Girolami
Bayesian Quadrature for Multiple Related Integrals. ICML (2018)
Richard Scalzo, David Kohn, Hugo K. H. Olierook, Gregory Houseman, Rohitash Chandra, Mark A. Girolami, Sally Cripps
Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success. CoRR (2018)
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, Jon Russ, Abigel Szodoray-Paradi, Farkas Szodoray-Paradi, Elena Tilova, Mark A. Girolami, Gabriel J. Brostow, Kate E. Jones
Bat detective - Deep learning tools for bat acoustic signal detection. PLoS Comput. Biol. 14 (3) (2018)
Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart, Aretha L. Teckentrup
How Deep Are Deep Gaussian Processes? J. Mach. Learn. Res. 19 (2018)
Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. CoRR (2017)
François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami
On the Sampling Problem for Kernel Quadrature. ICML (2017)
Chris J. Oates, Steven A. Niederer, Angela W. C. Lee, François-Xavier Briol, Mark A. Girolami
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models. NIPS (2017)
François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami
On the Sampling Problem for Kernel Quadrature. CoRR (2017)
Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami
Bayesian Probabilistic Numerical Methods. CoRR (2017)
Alexandros Beskos, Mark A. Girolami, Shiwei Lan, Patrick E. Farrell, Andrew M. Stuart
Geometric MCMC for infinite-dimensional inverse problems. J. Comput. Phys. 335 (2017)
Patrick R. Conrad, Mark A. Girolami, Simo Särkkä, Andrew M. Stuart, Konstantinos Zygalakis
Statistical analysis of differential equations: introducing probability measures on numerical solutions. Stat. Comput. 27 (4) (2017)
Alessandro Barp, François-Xavier Briol, Anthony D. Kennedy, Mark A. Girolami
Geometry and Dynamics for Markov Chain Monte Carlo. CoRR (2017)
Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami
Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems. CoRR (2016)
Chris J. Oates, Mark A. Girolami
Control Functionals for Quasi-Monte Carlo Integration. AISTATS (2016)
Shiwei Lan, Tan Bui-Thanh, Mike Christie, Mark A. Girolami
Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian Inverse Problems. J. Comput. Phys. 308 (2016)
Louis Ellam, Nicholas Zabaras, Mark A. Girolami
A Bayesian approach to multiscale inverse problems with on-the-fly scale determination. J. Comput. Phys. 326 (2016)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
Probabilistic Integration. CoRR (2015)
Seppo Virtanen, Mark A. Girolami
Ordinal Mixed Membership Models. ICML (2015)
Philipp Hennig, Michael A. Osborne, Mark A. Girolami
Probabilistic Numerics and Uncertainty in Computations. CoRR (2015)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. NIPS (2015)
Heiko Strathmann, Dino Sejdinovic, Mark A. Girolami
Unbiased Bayes for Big Data: Paths of Partial Posteriors. CoRR (2015)
Vassilios Stathopoulos, Veronica Zamora-Gutierrez, Kate E. Jones, Mark A. Girolami
Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel. AISTATS (2014)
Andrei Kramer, Vassilios Stathopoulos, Mark A. Girolami, Nicole Radde
mcmc_clib-an advanced MCMC sampling package for ode models. Bioinform. 30 (20) (2014)
Samuel Livingstone, Mark A. Girolami
Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions. Entropy 16 (6) (2014)
Oisin Mac Aodha, Vassilios Stathopoulos, Gabriel J. Brostow, Michael Terry, Mark A. Girolami, Kate E. Jones
Putting the Scientist in the Loop - Accelerating Scientific Progress with Interactive Machine Learning. ICPR (2014)
Maurizio Filippone, Mark A. Girolami
Pseudo-Marginal Bayesian Inference for Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 36 (11) (2014)
Oana Andrei, Muffy Calder, Matthew Higgs, Mark A. Girolami
Probabilistic Model Checking of DTMC Models of User Activity Patterns. QEST (2014)
Oana Andrei, Muffy Calder, Matthew Higgs, Mark A. Girolami
Probabilistic Model Checking of DTMC Models of User Activity Patterns. CoRR (2014)
Matthew Higgs, Alistair Morrison, Mark A. Girolami, Matthew Chalmers
Analysing user behaviour through dynamic population models. CHI Extended Abstracts (2013)
Maurizio Filippone, Mingjun Zhong, Mark A. Girolami
A comparative evaluation of stochastic-based inference methods for Gaussian process models. Mach. Learn. 93 (1) (2013)
Tom Diethe, Mark A. Girolami
Online Learning with (Multiple) Kernels: A Review. Neural Comput. 25 (3) (2013)
Maurizio Filippone, Mark A. Girolami
Exact-Approximate Bayesian Inference for Gaussian Processes. CoRR (2013)
Ke Yuan, Mark A. Girolami, Mahesan Niranjan
Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations. Neural Comput. 24 (6) (2012)
Neil D. Lawrence, Mark A. Girolami
Preface. AISTATS (2012)
Mingjun Zhong, Mark A. Girolami
A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices CoRR (2012)
Mingjun Zhong, Mark A. Girolami
A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices. ICML (2012)
Maurizio Filippone, Antonietta Mira, Mark A. Girolami
Discussion of the paper: "Sampling schemes for generalized linear Dirichlet process random effects models" by M. Kyung, J. Gill, and G. Casella. Stat. Methods Appl. 20 (3) (2011)
Tamara Polajnar, Theodoros Damoulas, Mark A. Girolami
Protein interaction sentence detection using multiple semantic kernels. J. Biomed. Semant. 2 (2011)
Tamara Polajnar, Simon Rogers, Mark A. Girolami
Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation. Int. J. Data Min. Bioinform. 5 (1) (2011)
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
ITFoM - The IT Future of Medicine. FET (2011)
Roberto Paredes, Mark A. Girolami
On the Use of Diagonal and Class-Dependent Weighted Distances for the Probabilistic k-Nearest Neighbor. IbPRIA (2011)
Mohammed Dakna, Keith Harris, Alexandros Kalousis, Sebastien Carpentier, Walter Kolch, Joost P. Schanstra, Marion Haubitz, Antonia Vlahou, Harald Mischak, Mark A. Girolami
Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers. BMC Bioinform. 11 (2010)
Ioannis Psorakis, Theodoros Damoulas, Mark A. Girolami
Multiclass relevance vector machines: sparsity and accuracy. IEEE Trans. Neural Networks 21 (10) (2010)
Simon Rogers, Mark A. Girolami, Tamara Polajnar
Semi-parametric analysis of multi-rater data. Stat. Comput. 20 (3) (2010)
Simon Rogers, Arto Klami, Janne Sinkkonen, Mark A. Girolami, Samuel Kaski
Infinite factorization of multiple non-parametric views. Mach. Learn. 79 (1-2) (2010)
Tamara Polajnar, Mark A. Girolami
Semi-supervised Prediction of Protein Interaction Sentences Exploiting Semantically Encoded Metrics. PRIB (2009)
Keith Harris, Lisa McMillan, Mark A. Girolami
Inferring Meta-covariates in Classification. PRIB (2009)
Keith Harris, Mark A. Girolami, Harald Mischak
Definition of Valid Proteomic Biomarkers: A Bayesian Solution. PRIB (2009)
Theodoros Damoulas, Mark A. Girolami
Combining feature spaces for classification. Pattern Recognit. 42 (11) (2009)
Tamara Polajnar, Simon Rogers, Mark A. Girolami
Classification of Protein Interaction Sentences via Gaussian Processes. PRIB (2009)
Simon Rogers, Richard A. Scheltema, Mark A. Girolami, Rainer Breitling
Probabilistic assignment of formulas to mass peaks in metabolomics experiments. Bioinform. 25 (4) (2009)
Ben Calderhead, Mark A. Girolami
Estimating Bayes factors via thermodynamic integration and population MCMC. Comput. Stat. Data Anal. 53 (12) (2009)
Mingjun Zhong, Mark A. Girolami
Reversible Jump MCMC for Non-Negative Matrix Factorization. AISTATS (2009)
Yiming Ying, Colin Campbell, Theodoros Damoulas, Mark A. Girolami
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm. PRIB (2009)
Theodoros Damoulas, Mark A. Girolami
Pattern recognition with a Bayesian kernel combination machine. Pattern Recognit. Lett. 30 (1) (2009)
Rónán Daly, Kieron D. Edwards, John S. O'Neill, J. Stuart Aitken, Andrew J. Millar, Mark A. Girolami
Using Higher-Order Dynamic Bayesian Networks to Model Periodic Data from the Circadian Clock of Arabidopsis Thaliana. PRIB (2009)
Yiming Ying, Colin Campbell, Mark A. Girolami
Analysis of SVM with Indefinite Kernels. NIPS (2009)
Vladislav Vyshemirsky, Mark A. Girolami
Bayesian ranking of biochemical system models. Bioinform. 24 (6) (2008)
Vladislav Vyshemirsky, Mark A. Girolami
BioBayes: A software package for Bayesian inference in systems biology. Bioinform. 24 (17) (2008)
Mark A. Girolami
Bayesian inference for differential equations. Theor. Comput. Sci. 408 (1) (2008)
Theodoros Damoulas, Yiming Ying, Mark A. Girolami, Colin Campbell
Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins. ICMLA (2008)
Theodoros Damoulas, Mark A. Girolami
Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection. Bioinform. 24 (10) (2008)
Mingjun Zhong, Fabien Lotte, Mark A. Girolami, Anatole Lécuyer
Classifying EEG for brain computer interfaces using Gaussian processes. Pattern Recognit. Lett. 29 (3) (2008)
Simon Rogers, Mark A. Girolami, Walter Kolch, Katrina M. Waters, Tao Liu, Brian Thrall, H. Steven Wiley
Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models. Bioinform. 24 (24) (2008)
Ben Calderhead, Mark A. Girolami, Neil D. Lawrence
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes. NIPS (2008)
Ian M. Overton, Gianandrea Padovani, Mark A. Girolami, Geoffrey J. Barton
ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction. Bioinform. 24 (7) (2008)
Vladislav Vyshemirsky, Mark A. Girolami
Bayesian ranking of biochemical system models. Bioinform. 24 (20) (2008)
Nicola Lama, Mark A. Girolami
vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R. Bioinform. 24 (1) (2008)
Dongshan Xing, Mark A. Girolami
Employing Latent Dirichlet Allocation for fraud detection in telecommunications. Pattern Recognit. Lett. 28 (13) (2007)
S. Manocha, Mark A. Girolami
An empirical analysis of the probabilistic K-nearest neighbour classifier. Pattern Recognit. Lett. 28 (13) (2007)
Oliver Sharma, Mark A. Girolami, Joseph S. Sventek
Detecting worm variants using machine learning. CoNEXT (2007)
Simon Rogers, Raya Khanin, Mark A. Girolami
Bayesian model-based inference of transcription factor activity. BMC Bioinform. 8 (S-2) (2007)
Simon Rogers, Mark A. Girolami
Multi-class Semi-supervised Learning with the e-truncated Multinomial Probit Gaussian Process. Gaussian Processes in Practice (2007)
Mark A. Girolami, Mingjun Zhong
Data Integration for Classification Problems Employing Gaussian Process Priors. NIPS (2006)
Robert Jenssen, Torbjørn Eltoft, Mark A. Girolami, Deniz Erdogmus
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm. NIPS (2006)
Mark A. Girolami, Simon Rogers
Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. Neural Comput. 18 (8) (2006)
Anna Szymkowiak-Have, Mark A. Girolami, Jan Larsen
Clustering via kernel decomposition. IEEE Trans. Neural Networks 17 (1) (2006)
Gavin C. Cawley, Nicola L. C. Talbot, Mark A. Girolami
Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation. NIPS (2006)
Simon Rogers, Mark A. Girolami, Colin Campbell, Rainer Breitling
The Latent Process Decomposition of cDNA Microarray Data Sets. IEEE ACM Trans. Comput. Biol. Bioinform. 2 (2) (2005)
Simon Rogers, Mark A. Girolami, Ronald Krebs, Harald Mischak
Disease Classification from Capillary Electrophoresis: Mass Spectrometry. ICAPR (1) (2005)
Leif Azzopardi, Mark A. Girolami, Malcolm K. Crowe
Probabilistic hyperspace analogue to language. SIGIR (2005)
Mark A. Girolami, Ata Kabán
Sequential Activity Profiling: Latent Dirichlet Allocation of Markov Chains. Data Min. Knowl. Discov. 10 (3) (2005)
Simon Rogers, Mark A. Girolami
A Bayesian regression approach to the inference of regulatory networks from gene expression data. Bioinform. 21 (14) (2005)
Mark A. Girolami, Simon Rogers
Hierarchic Bayesian models for kernel learning. ICML (2005)

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Publication Activity (10 Years)

Top Topics

Top Venues

Mark A. Girolami