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Mark Girolami
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- affiliation: University of Cambridge, UK
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2020 – today
- 2024
[j89]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): 95-109 (2024)- [j88]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: 108637 (2024) - [j87]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: 113293 (2024) - [j86]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: 112272 (2024) - [c61]Hanlin Yu, Marcelo Hartmann, Bernardo Williams Moreno Sanchez, Mark Girolami, Arto Klami:
Riemannian Laplace Approximation with the Fisher Metric. AISTATS 2024: 820-828 - [i49]Ömer Deniz Akyildiz, Mark Girolami, Andrew M. Stuart, Arnaud Vadeboncoeur:
Efficient Prior Calibration From Indirect Data. CoRR abs/2405.17955 (2024) - [i48]Justin Bunker, Mark Girolami, Hefin Lambley, Andrew M. Stuart, Tim Sullivan:
Autoencoders in Function Space. CoRR abs/2408.01362 (2024) - [i47]Alex Glyn-Davies, Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami:
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling. CoRR abs/2409.06560 (2024) - 2023
- [j85]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): 821-848 (2023) - [j84]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: 112369 (2023) - [j83]Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan:
Theoretical Guarantees for the Statistical Finite Element Method. SIAM/ASA J. Uncertain. Quantification 11(4): 1278-1307 (2023) - [j82]Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken:
Bayesian learning via neural Schrödinger-Föllmer flows. Stat. Comput. 33(1): 3 (2023) - [j81]Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami:
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. Trans. Mach. Learn. Res. 2023 (2023) - [c60]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: 1-5 - [c59]Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz:
Random Grid Neural Processes for Parametric Partial Differential Equations. ICML 2023: 34759-34778 - [i46]Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz:
Random Grid Neural Processes for Parametric Partial Differential Equations. CoRR abs/2301.11040 (2023) - [i45]Thomas Gaskin, Grigorios A. Pavliotis, Mark Girolami:
Inferring networks from time series: a neural approach. CoRR abs/2303.18059 (2023) - [i44]Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross, Andrew B. Duncan, Mark Girolami:
Encoding Domain Expertise into Multilevel Models for Source Location. CoRR abs/2305.08657 (2023) - [i43]Marcelo Hartmann, Bernardo Williams, Hanlin Yu, Mark Girolami, Alessandro Barp, Arto Klami:
Warped geometric information on the optimisation of Euclidean functions. CoRR abs/2308.08305 (2023) - [i42]Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Mark Girolami, Arto Klami:
Riemannian Laplace Approximation with the Fisher Metric. CoRR abs/2311.02766 (2023) - [i41]Andrea Marinoni, Pietro Lio', Alessandro Barp, Christian Jutten, Mark Girolami:
Improving embedding of graphs with missing data by soft manifolds. CoRR abs/2311.17598 (2023) - 2022
- [j80]Connor Duffin, Edward Cripps, Thomas Stemler, Mark Girolami:
Low-rank statistical finite elements for scalable model-data synthesis. J. Comput. Phys. 463: 111261 (2022) - [j79]Ömer Deniz Akyildiz, Connor Duffin, Sotirios Sabanis, Mark Girolami:
Statistical Finite Elements via Langevin Dynamics. SIAM/ASA J. Uncertain. Quantification 10(4): 1560-1585 (2022) - [j78]Alex Glyn-Davies, Mark Girolami:
Anomaly detection in streaming data with gaussian process based stochastic differential equations. Pattern Recognit. Lett. 153: 254-260 (2022) - [j77]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: 74-80 (2022) - [c58]Marcelo Hartmann, Mark Girolami, Arto Klami:
Lagrangian manifold Monte Carlo on Monge patches. AISTATS 2022: 4764-4781 - [i40]Toni Karvonen, Fehmi Cirak, Mark Girolami:
Error analysis for a statistical finite element method. CoRR abs/2201.07543 (2022) - [i39]Marcelo Hartmann, Mark Girolami, Arto Klami:
Lagrangian Manifold Monte Carlo on Monge Patches. CoRR abs/2202.00755 (2022) - [i38]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 abs/2203.10592 (2022) - [i37]Lawrence 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 abs/2204.12404 (2022) - [i36]Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak:
Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs. CoRR abs/2208.04856 (2022) - [i35]Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami, Lester Mackey:
Targeted Separation and Convergence with Kernel Discrepancies. CoRR abs/2209.12835 (2022) - [i34]Thomas Gaskin, Grigorios A. Pavliotis, Mark Girolami:
Neural parameter calibration for large-scale multi-agent models. CoRR abs/2209.13565 (2022) - [i33]Alex Glyn-Davies, Connor Duffin, Ömer Deniz Akyildiz, Mark Girolami:
$Φ$-DVAE: Learning Physically Interpretable Representations with Nonlinear Filtering. CoRR abs/2209.15609 (2022) - [i32]Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami:
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. CoRR abs/2211.09196 (2022) - 2021
- [j76]Ashley Scillitoe, Pranay Seshadri, Mark Girolami:
Uncertainty quantification for data-driven turbulence modelling with Mondrian forests. J. Comput. Phys. 430: 110116 (2021) - [j75]George Wynne, François-Xavier Briol, Mark Girolami:
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness. J. Mach. Learn. Res. 22: 123:1-123:40 (2021) - [j74]Toni Karvonen, Chris J. Oates, Mark Girolami:
Integration in reproducing kernel Hilbert spaces of Gaussian kernels. Math. Comput. 90(331): 2209-2233 (2021) - [j73]Steven A. Niederer, Michael S. Sacks, Mark Girolami, Karen Willcox:
Scaling digital twins from the artisanal to the industrial. Nat. Comput. Sci. 1(5): 313-320 (2021) - [i31]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 abs/2103.13729 (2021) - [i30]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 abs/2105.03682 (2021) - [i29]B. Boys, Tim J. Dodwell, Mark Hobbs, Mark Girolami:
PeriPy - A High Performance OpenCL Peridynamics Package. CoRR abs/2105.04150 (2021) - [i28]Connor Duffin, Edward Cripps, Thomas Stemler, Mark Girolami:
Low-rank statistical finite elements for scalable model-data synthesis. CoRR abs/2109.04757 (2021) - [i27]Ömer Deniz Akyildiz, Connor Duffin, Sotirios Sabanis, Mark Girolami:
Statistical Finite Elements via Langevin Dynamics. CoRR abs/2110.11131 (2021) - [i26]Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan:
Theoretical Guarantees for the Statistical Finite Element Method. CoRR abs/2111.07691 (2021) - [i25]Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken:
Bayesian Learning via Neural Schrödinger-Föllmer Flows. CoRR abs/2111.10510 (2021) - [i24]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 abs/2112.04388 (2021) - 2020
- [j72]Karla Monterrubio-Gómez, Lassi Roininen, Sara Wade, Theodoros Damoulas, Mark Girolami:
Posterior inference for sparse hierarchical non-stationary models. Comput. Stat. Data Anal. 148: 106954 (2020) - [c57]Seppo Virtanen, Mark Girolami:
Dynamic content based ranking. AISTATS 2020: 2315-2324 - [i23]George Wynne, François-Xavier Briol, Mark A. Girolami:
Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models. CoRR abs/2001.10818 (2020) - [i22]Toni Karvonen, Chris J. Oates, Mark Girolami:
Integration in reproducing kernel Hilbert spaces of Gaussian kernels. CoRR abs/2004.12654 (2020) - [i21]Rebecca Ward, Ruchi Choudhary, Alastair Gregory, Mark Girolami:
Continuous calibration of a digital twin: comparison of particle filter and Bayesian calibration approaches. CoRR abs/2011.09810 (2020) - [i20]Pranay Seshadri, Andrew B. Duncan, George Thorne, Geoffrey T. Parks, Mark Girolami:
Bayesian Assessments of Aeroengine Performance. CoRR abs/2011.14698 (2020)
2010 – 2019
- 2019
- [j71]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: 183996-184007 (2019) - [j70]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: 248-265 (2019) - [j69]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): 1181-1183 (2019) - [j68]Jon Cockayne, Chris J. Oates, Timothy John Sullivan, Mark A. Girolami:
Bayesian Probabilistic Numerical Methods. SIAM Rev. 61(4): 756-789 (2019) - [c56]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: 1011-1021 - [c55]Seppo Virtanen, Mark A. Girolami:
Precision-Recall Balanced Topic Modelling. NeurIPS 2019: 6747-6756 - [c54]Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey:
Minimum Stein Discrepancy Estimators. NeurIPS 2019: 12964-12976 - [c53]Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark A. Girolami:
Multi-resolution Multi-task Gaussian Processes. NeurIPS 2019: 14025-14035 - [i19]François-Xavier Briol, Alessandro Barp, Andrew B. Duncan, Mark A. Girolami:
Statistical Inference for Generative Models with Maximum Mean Discrepancy. CoRR abs/1906.05944 (2019) - [i18]Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey:
Minimum Stein Discrepancy Estimators. CoRR abs/1906.08283 (2019) - [i17]Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark A. Girolami:
Multi-resolution Multi-task Gaussian Processes. CoRR abs/1906.08344 (2019) - [i16]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 abs/1907.07037 (2019) - 2018
- [j67]Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart, Aretha L. Teckentrup:
How Deep Are Deep Gaussian Processes? J. Mach. Learn. Res. 19: 54:1-54:46 (2018) - [j66]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) - [c52]Xiaoyue Xi, François-Xavier Briol, Mark A. Girolami:
Bayesian Quadrature for Multiple Related Integrals. ICML 2018: 5369-5378 - [i15]Xiaoyue Xi, François-Xavier Briol, Mark A. Girolami:
Bayesian Quadrature for Multiple Related Integrals. CoRR abs/1801.04153 (2018) - [i14]Jon Cockayne, Chris J. Oates, Mark A. Girolami:
A Bayesian Conjugate Gradient Method. CoRR abs/1801.05242 (2018) - [i13]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic:
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". CoRR abs/1811.10275 (2018) - [i12]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 abs/1812.00318 (2018) - 2017
- [j65]Alexandros Beskos, Mark A. Girolami, Shiwei Lan, Patrick E. Farrell, Andrew M. Stuart:
Geometric MCMC for infinite-dimensional inverse problems. J. Comput. Phys. 335: 327-351 (2017) - [j64]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): 1065-1082 (2017) - [c51]François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami:
On the Sampling Problem for Kernel Quadrature. ICML 2017: 586-595 - [c50]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: 110-118 - [i11]Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami:
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. CoRR abs/1701.04006 (2017) - [i10]Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami:
Bayesian Probabilistic Numerical Methods. CoRR abs/1702.03673 (2017) - [i9]Alessandro Barp, François-Xavier Briol, Anthony D. Kennedy, Mark A. Girolami:
Geometry and Dynamics for Markov Chain Monte Carlo. CoRR abs/1705.02891 (2017) - [i8]François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami:
On the Sampling Problem for Kernel Quadrature. CoRR abs/1706.03369 (2017) - 2016
- [j63]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: 81-101 (2016) - [j62]Louis Ellam, Nicholas Zabaras, Mark A. Girolami:
A Bayesian approach to multiscale inverse problems with on-the-fly scale determination. J. Comput. Phys. 326: 115-140 (2016) - [c49]Chris J. Oates, Mark A. Girolami:
Control Functionals for Quasi-Monte Carlo Integration. AISTATS 2016: 56-65 - [i7]Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami:
Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems. CoRR abs/1605.07811 (2016) - 2015
- [c48]Seppo Virtanen, Mark A. Girolami:
Ordinal Mixed Membership Models. ICML 2015: 588-596 - [c47]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne:
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. NIPS 2015: 1162-1170 - [i6]Heiko Strathmann, Dino Sejdinovic, Mark A. Girolami:
Unbiased Bayes for Big Data: Paths of Partial Posteriors. CoRR abs/1501.03326 (2015) - [i5]Philipp Hennig, Michael A. Osborne, Mark A. Girolami:
Probabilistic Numerics and Uncertainty in Computations. CoRR abs/1506.01326 (2015) - [i4]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic:
Probabilistic Integration. CoRR abs/1512.00933 (2015) - 2014
- [j61]Andrei Kramer, Vassilios Stathopoulos, Mark A. Girolami, Nicole Radde:
mcmc_clib-an advanced MCMC sampling package for ode models. Bioinform. 30(20): 2991-2992 (2014) - [j60]Samuel Livingstone, Mark A. Girolami:
Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions. Entropy 16(6): 3074-3102 (2014) - [j59]Maurizio Filippone, Mark A. Girolami:
Pseudo-Marginal Bayesian Inference for Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 36(11): 2214-2226 (2014) - [c46]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: 913-921 - [c45]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: 9-17 - [c44]Oana Andrei, Muffy Calder, Matthew Higgs, Mark A. Girolami:
Probabilistic Model Checking of DTMC Models of User Activity Patterns. QEST 2014: 138-153 - [i3]Oana Andrei, Muffy Calder, Matthew Higgs, Mark A. Girolami:
Probabilistic Model Checking of DTMC Models of User Activity Patterns. CoRR abs/1403.6678 (2014) - 2013
- [j58]Maurizio Filippone, Mingjun Zhong, Mark A. Girolami:
A comparative evaluation of stochastic-based inference methods for Gaussian process models. Mach. Learn. 93(1): 93-114 (2013) - [j57]Tom Diethe, Mark A. Girolami:
Online Learning with (Multiple) Kernels: A Review. Neural Comput. 25(3): 567-625 (2013) - [c43]Matthew Higgs, Alistair Morrison, Mark A. Girolami, Matthew Chalmers:
Analysing user behaviour through dynamic population models. CHI Extended Abstracts 2013: 271-276 - [i2]Maurizio Filippone, Mark A. Girolami:
Exact-Approximate Bayesian Inference for Gaussian Processes. CoRR abs/1310.0740 (2013) - 2012
- [j56]Ke Yuan, Mark A. Girolami, Mahesan Niranjan:
Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations. Neural Comput. 24(6): 1462-1486 (2012) - [c42]Mingjun Zhong, Mark A. Girolami:
A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices. ICML 2012 - [c41]Neil D. Lawrence, Mark A. Girolami:
Preface. AISTATS 2012 - [e6]Neil D. Lawrence, Mark A. Girolami:
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2012, La Palma, Canary Islands, Spain, April 21-23, 2012. JMLR Proceedings 22, JMLR.org 2012 [contents] - [i1]Mingjun Zhong, Mark A. Girolami:
A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices. CoRR abs/1206.4666 (2012) - 2011
- [b1]Simon Rogers, Mark A. Girolami:
A First Course in Machine Learning. Chapman and Hall / CRC machine learning and pattern recognition series, CRC Press 2011, ISBN 978-1-43-982414-6, pp. I-XX, 1-285 - [j55]Tamara Polajnar, Theodoros Damoulas, Mark A. Girolami:
Protein interaction sentence detection using multiple semantic kernels. J. Biomed. Semant. 2: 1 (2011) - [j54]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): 52-72 (2011) - [j53]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): 295-297 (2011) - [c40]Roberto Paredes, Mark A. Girolami:
On the Use of Diagonal and Class-Dependent Weighted Distances for the Probabilistic k-Nearest Neighbor. IbPRIA 2011: 265-272 - [c39]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: 26-29 - [e5]Timo Honkela, Wlodzislaw Duch, Mark A. Girolami, Samuel Kaski:
Artificial Neural Networks and Machine Learning - ICANN 2011 - 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I. Lecture Notes in Computer Science 6791, Springer 2011, ISBN 978-3-642-21734-0 [contents] - [e4]Timo Honkela, Wlodzislaw Duch, Mark A. Girolami, Samuel Kaski:
Artificial Neural Networks and Machine Learning - ICANN 2011 - 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II. Lecture Notes in Computer Science 6792, Springer 2011, ISBN 978-3-642-21737-1 [contents] - 2010
- [j52]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: 594 (2010) - [j51]Simon Rogers, Arto Klami, Janne Sinkkonen, Mark A. Girolami, Samuel Kaski:
Infinite factorization of multiple non-parametric views. Mach. Learn. 79(1-2): 201-226 (2010) - [j50]Simon Rogers, Mark A. Girolami, Tamara Polajnar:
Semi-parametric analysis of multi-rater data. Stat. Comput. 20(3): 317-334 (2010) - [j49]Ioannis Psorakis, Theodoros Damoulas, Mark A. Girolami:
Multiclass relevance vector machines: sparsity and accuracy. IEEE Trans. Neural Networks 21(10): 1588-1598 (2010) - [p1]Mark Girolami, Ben Calderhead, Vladislav Vyshemirsky:
System Identification and Model Ranking: The Bayesian Perspective. Learning and Inference in Computational Systems Biology 2010: 201-230 - [e3]Neil D. Lawrence, Mark A. Girolami, Magnus Rattray, Guido Sanguinetti:
Learning and Inference in Computational Systems Biology. Computational molecular biology, MIT Press 2010, ISBN 978-0-262-01386-4 [contents]
2000 – 2009
- 2009
- [j48]Simon Rogers, Richard A. Scheltema, Mark A. Girolami, Rainer Breitling:
Probabilistic assignment of formulas to mass peaks in metabolomics experiments. Bioinform. 25(4): 512-518 (2009) - [j47]Ben Calderhead, Mark A. Girolami:
Estimating Bayes factors via thermodynamic integration and population MCMC. Comput. Stat. Data Anal. 53(12): 4028-4045 (2009) - [j46]Theodoros Damoulas, Mark A. Girolami:
Combining feature spaces for classification. Pattern Recognit. 42(11): 2671-2683 (2009) - [j45]Theodoros Damoulas, Mark A. Girolami:
Pattern recognition with a Bayesian kernel combination machine. Pattern Recognit. Lett. 30(1): 46-54 (2009) - [c38]Yiming Ying, Colin Campbell, Mark A. Girolami:
Analysis of SVM with Indefinite Kernels. NIPS 2009: 2205-2213 - [c37]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: 67-78 - [c36]Keith Harris, Mark A. Girolami, Harald Mischak:
Definition of Valid Proteomic Biomarkers: A Bayesian Solution. PRIB 2009: 137-149 - [c35]Keith Harris, Lisa McMillan, Mark A. Girolami:
Inferring Meta-covariates in Classification. PRIB 2009: 150-161 - [c34]Tamara Polajnar, Mark A. Girolami:
Semi-supervised Prediction of Protein Interaction Sentences Exploiting Semantically Encoded Metrics. PRIB 2009: 270-281 - [c33]Tamara Polajnar, Simon Rogers, Mark A. Girolami:
Classification of Protein Interaction Sentences via Gaussian Processes. PRIB 2009: 282-292 - [c32]Yiming Ying, Colin Campbell, Theodoros Damoulas, Mark A. Girolami:
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm. PRIB 2009: 427-438 - [c31]Mingjun Zhong, Mark A. Girolami:
Reversible Jump MCMC for Non-Negative Matrix Factorization. AISTATS 2009: 663-670 - [e2]Visakan Kadirkamanathan, Guido Sanguinetti, Mark A. Girolami, Mahesan Niranjan, Josselin Noirel:
Pattern Recognition in Bioinformatics, 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings. Lecture Notes in Computer Science 5780, Springer 2009, ISBN 978-3-642-04030-6 [contents] - 2008
- [j44]Nicola Lama, Mark A. Girolami:
vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R. Bioinform. 24(1): 135-136 (2008) - [j43]Vladislav Vyshemirsky, Mark A. Girolami:
Bayesian ranking of biochemical system models. Bioinform. 24(6): 833-839 (2008) - [j42]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): 901-907 (2008) - [j41]Theodoros Damoulas, Mark A. Girolami:
Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection. Bioinform. 24(10): 1264-1270 (2008) - [j40]Vladislav Vyshemirsky, Mark A. Girolami:
BioBayes: A software package for Bayesian inference in systems biology. Bioinform. 24(17): 1933-1934 (2008) - [j39]Vladislav Vyshemirsky, Mark A. Girolami:
Bayesian ranking of biochemical system models. Bioinform. 24(20): 2421 (2008) - [j38]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): 2894-2900 (2008) - [j37]Mingjun Zhong, Fabien Lotte, Mark A. Girolami, Anatole Lécuyer:
Classifying EEG for brain computer interfaces using Gaussian processes. Pattern Recognit. Lett. 29(3): 354-359 (2008) - [j36]Mark A. Girolami:
Bayesian inference for differential equations. Theor. Comput. Sci. 408(1): 4-16 (2008) - [c30]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: 577-582 - [c29]Ben Calderhead, Mark A. Girolami, Neil D. Lawrence:
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes. NIPS 2008: 217-224 - 2007
- [j35]Simon Rogers, Raya Khanin, Mark A. Girolami:
Bayesian model-based inference of transcription factor activity. BMC Bioinform. 8(S-2) (2007) - [j34]Dongshan Xing, Mark A. Girolami:
Employing Latent Dirichlet Allocation for fraud detection in telecommunications. Pattern Recognit. Lett. 28(13): 1727-1734 (2007) - [j33]S. Manocha, Mark A. Girolami:
An empirical analysis of the probabilistic K-nearest neighbour classifier. Pattern Recognit. Lett. 28(13): 1818-1824 (2007) - [c28]Oliver Sharma, Mark A. Girolami, Joseph S. Sventek:
Detecting worm variants using machine learning. CoNEXT 2007: 2 - [c27]Simon Rogers, Mark A. Girolami:
Multi-class Semi-supervised Learning with the e-truncated Multinomial Probit Gaussian Process. Gaussian Processes in Practice 2007: 17-32 - 2006
- [j32]Mark A. Girolami, Simon Rogers:
Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. Neural Comput. 18(8): 1790-1817 (2006) - [j31]Anna Szymkowiak-Have, Mark A. Girolami, Jan Larsen:
Clustering via kernel decomposition. IEEE Trans. Neural Networks 17(1): 256-264 (2006) - [c26]Gavin C. Cawley, Nicola L. C. Talbot, Mark A. Girolami:
Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation. NIPS 2006: 209-216 - [c25]Mark A. Girolami, Mingjun Zhong:
Data Integration for Classification Problems Employing Gaussian Process Priors. NIPS 2006: 465-472 - [c24]Robert Jenssen, Torbjørn Eltoft, Mark A. Girolami, Deniz Erdogmus:
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm. NIPS 2006: 633-640 - 2005
- [j30]Simon Rogers, Mark A. Girolami:
A Bayesian regression approach to the inference of regulatory networks from gene expression data. Bioinform. 21(14): 3131-3137 (2005) - [j29]Mark A. Girolami, Ata Kabán:
Sequential Activity Profiling: Latent Dirichlet Allocation of Markov Chains. Data Min. Knowl. Discov. 10(3): 175-196 (2005) - [j28]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): 143-156 (2005) - [c23]Simon Rogers, Mark A. Girolami, Ronald Krebs, Harald Mischak:
Disease Classification from Capillary Electrophoresis: Mass Spectrometry. ICAPR (1) 2005: 183-191 - [c22]Mark A. Girolami, Simon Rogers:
Hierarchic Bayesian models for kernel learning. ICML 2005: 241-248 - [c21]Leif Azzopardi, Mark A. Girolami, Malcolm K. Crowe:
Probabilistic hyperspace analogue to language. SIGIR 2005: 575-576 - 2004
- [j27]Mark A. Girolami, Rainer Breitling:
Biologically valid linear factor models of gene expression. Bioinform. 20(17): 3021-3033 (2004) - [j26]Chao He, Mark A. Girolami, Gary Ross:
Employing optimized combinations of one-class classifiers for automated currency validation. Pattern Recognit. 37(6): 1085-1096 (2004) - [j25]Chao He, Mark A. Girolami:
Novelty detection employing an L2 optimal non-parametric density estimator. Pattern Recognit. Lett. 25(12): 1389-1397 (2004) - [c20]Ali Al-Shahib, Chao He, Aik Choon Tan, Mark A. Girolami, David R. Gilbert:
An Assessment of Feature Relevance in Predicting Protein Function from Sequence. IDEAL 2004: 52-57 - [c19]Leif Azzopardi, Mark A. Girolami, Cornelis Joost van Rijsbergen:
User biased document language modelling. SIGIR 2004: 542-543 - 2003
- [j24]Ella Bingham, Ata Kabán, Mark A. Girolami:
Topic Identification in Dynamical Text by Complexity Pursuit. Neural Process. Lett. 17(1): 69-83 (2003) - [j23]Mark A. Girolami, Chao He:
Probability Density Estimation from Optimally Condensed Data Samples. IEEE Trans. Pattern Anal. Mach. Intell. 25(10): 1253-1264 (2003) - [c18]Mark A. Girolami, Ata Kabán:
Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles. NIPS 2003: 9-16 - [c17]Leif Azzopardi, Mark A. Girolami, Keith van Rijsbergen:
Investigating the relationship between language model perplexity and IR precision-recall measures. SIGIR 2003: 369-370 - [c16]Mark A. Girolami, Ata Kabán:
On an equivalence between PLSI and LDA. SIGIR 2003: 433-434 - 2002
- [j22]Mark A. Girolami:
Latent variable models for the topographic organisation of discrete and strictly positive data. Neurocomputing 48(1-4): 185-198 (2002) - [j21]Ata Kabán, Mark A. Girolami:
A Dynamic Probabilistic Model to Visualise Topic Evolution in Text Streams. J. Intell. Inf. Syst. 18(2-3): 107-125 (2002) - [j20]Alexei Vinokourov, Mark A. Girolami:
A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections. J. Intell. Inf. Syst. 18(2-3): 153-172 (2002) - [j19]Mark A. Girolami:
Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem. Neural Comput. 14(3): 669-688 (2002) - [j18]Ata Kabán, Mark A. Girolami:
Fast Extraction of Semantic Features from a Latent Semantic Indexed Text Corpus. Neural Process. Lett. 15(1): 31-43 (2002) - [j17]Fabio Crestani, Mark A. Girolami, C. J. van Rijsbergen:
Report on the 24th European colloquium on information retrieval research (ECIR 2002). SIGIR Forum 36(1): 6-9 (2002) - [j16]Fabio Crestani, Mark A. Girolami:
Report on the 24th European Colloquium on Information Retrieval Research. SIGMOD Rec. 31(3): 77-80 (2002) - [j15]Mark A. Girolami:
Mercer kernel-based clustering in feature space. IEEE Trans. Neural Networks 13(3): 780-784 (2002) - [c15]Ata Kabán, Peter Tiño, Mark A. Girolami:
A General Framework for a Principled Hierarchical Visualization of Multivariate Data. IDEAL 2002: 518-523 - [e1]Fabio Crestani, Mark A. Girolami, C. J. van Rijsbergen:
Advances in Information Retrieval, 24th BCS-IRSG European Colloquium on IR Research Glasgow, UK, March 25-27, 2002 Proceedings. Lecture Notes in Computer Science 2291, Springer 2002, ISBN 3-540-43343-0 [contents] - 2001
- [j14]Roman Rosipal, Mark A. Girolami, Leonard J. Trejo, Andrzej Cichocki:
Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression. Neural Comput. Appl. 10(3): 231-243 (2001) - [j13]Roman Rosipal, Mark A. Girolami:
An Expectation-Maximization Approach to Nonlinear Component Analysis. Neural Comput. 13(3): 505-510 (2001) - [j12]Mark A. Girolami:
A Variational Method for Learning Sparse and Overcomplete Representations. Neural Comput. 13(11): 2517-2532 (2001) - [j11]Ata Kabán, Mark A. Girolami:
A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data. IEEE Trans. Pattern Anal. Mach. Intell. 23(8): 859-872 (2001) - [j10]Mark A. Girolami:
The topographic organization and visualization of binary data using multivariate-Bernoulli latent variable models. IEEE Trans. Neural Networks 12(6): 1367-1374 (2001) - [c14]Ella Bingham, Ata Kabán, Mark A. Girolami:
Finding Topics in Dynamical Text: Application to Chat Line Discussions. WWW Posters 2001 - 2000
- [c13]Allan Kardec Barros, Roman Rosipal, Mark A. Girolami, Georg Dorffner, Noboru Ohnishi:
Extraction of Sleep-Spindles from the Electroencephalogram (EEG). ANNIMAB 2000: 125-130 - [c12]Roman Rosipal, Mark A. Girolami, Leonard J. Trejo:
Kernel PCA Feature Extraction of Event-Related Potentials for Human Signal Detection Performance. ANNIMAB 2000: 321-326 - [c11]Mark A. Girolami, Alexei Vinokourov, Ata Kabán:
The Organization and Visualization of Document Corpora: A Probabilistic Approach. DEXA Workshop 2000: 558-564 - [c10]Mark A. Girolami:
A generative model for sparse discrete binary data with non-uniform categorical priors. ESANN 2000: 1-6 - [c9]Alexei Vinokourov, Mark A. Girolami:
Probabilistic Hierarchical Clustering Method for Organizing Collections of Text Documents. ICPR 2000: 2182-2185 - [c8]Ata Kabán, Mark A. Girolami:
Initialized and Guided EM-Clustering of Sparse Binary Data with Application to Text Based Documents. ICPR 2000: 2744-2747
1990 – 1999
- 1999
- [j9]Te-Won Lee, Mark A. Girolami, Terrence J. Sejnowski:
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources. Neural Comput. 11(2): 417-441 (1999) - [j8]Te-Won Lee, Michael S. Lewicki, Mark A. Girolami, Terrence J. Sejnowski:
Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Process. Lett. 6(4): 87-90 (1999) - 1998
- [j7]Mark A. Girolami:
A nonlinear model of the binaural cocktail party effect. Neurocomputing 22(1-3): 201-215 (1998) - [j6]Mark A. Girolami:
An Alternative Perspective on Adaptive Independent Component Analysis Algorithms. Neural Comput. 10(8): 2103-2114 (1998) - [j5]Mark A. Girolami:
The Latent Variable Data Model for Exploratory Data Analysis and Visualisation: A Generalisation of the Nonlinear Infomax Algorithm. Neural Process. Lett. 8(1): 27-39 (1998) - [j4]Mark A. Girolami, Andrzej Cichocki, Shun-ichi Amari:
A common neural-network model for unsupervised exploratory data analysis and independent component analysis. IEEE Trans. Neural Networks 9(6): 1495-1501 (1998) - [c7]Mark A. Girolami:
Noise reduction and speech enhancement via temporal anti-Hebbian learning. ICASSP 1998: 1233-1236 - 1997
- [j3]Mark A. Girolami, Colin Fyfe:
Stochastic ICA Contrast Maximisation Using Oja's Nonlinear PCA Algorithm. Int. J. Neural Syst. 8(5-6): 661-678 (1997) - [j2]Mark A. Girolami, Colin Fyfe:
An extended exploratory projection pursuit network with linear and nonlinear anti-hebbian lateral connections applied to the cocktail party problem. Neural Networks 10(9): 1607-1618 (1997) - [c6]Mark Girolami, Colin Fyfe:
Multivariate Density Factorization for Independent Component Analysis: An Unsupervised Artificial Neural Network Approach. AISTATS 1997: 223-230 - [c5]Mark A. Girolami, Colin Fyfe:
Independence is far from normal. ESANN 1997 - [c4]Mark Girolami:
Principal Components Identify MLP Hidden Layer Size for Optimal Generalisation Performance. ICANNGA 1997: 40-43 - [c3]Mark Girolami, Colin Fyfe:
Fahlman-Type Activation Functions Applied to Nonlinear PCA Networks Provide a Generalised Independent Component Analysis. ICANNGA 1997: 112-115 - [c2]Mark A. Girolami, Colin Fyfe:
Kurtosis extrema and identification of independent components: a neural network approach. ICASSP 1997: 3329-3332 - [c1]Mark A. Girolami, Colin Fyfe:
Generalised independent component analysis through unsupervised learning with emergent Bussgang properties. ICNN 1997: 1788-1791 - 1996
- [j1]Mark A. Girolami, Colin Fyfe:
A Temporal Model of Linear Anti-Hebbian Learning. Neural Process. Lett. 4(3): 139-148 (1996)
Coauthor Index
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