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Journal and Conference Publications
2008
 Vyshemirsky, V., Girolami, M., Bayesian Ranking of Biochemical System Models. Bioinformatics
Lama,
N., Girolami, M., vbmp: Variational Bayesian Multinomial Probit Regression for multi-class classification in R. Bioinformatics
2007
Girolami, M., Zhong, M., Data Integration
for Classification problems Employing Gaussian Process Priors. Twentieth
Annual Conference on Neural Information Processing Systems - NIPS 2006 MIT Press. Supplementary
Material Bioconductor R Package Available Here
Cawley,
G., Talbot, N., Girolami, M., Sparse Multinomial Logistic
Regression via Bayesian Regularisation using a Laplace
Prior. Twentieth Annual Conference on Neural Information Processing
Systems - NIPS 2006 MIT Press.
Jensen,
R., Eltoft, T., Girolami,
M., Erdogmus, D., Kernel Maximum Entropy Data
Transformation and an Enhanced Spectral Clustering Algorithm. Twentieth Annual Conference on Neural Information Processing
Systems - NIPS 2006 MIT Press.
Manocha,
S & Girolami, M., An
Empirical Analysis of the Probabilistic K-Nearest Neighbour
Classifier, Pattern
Recognition Letters, 28(13),pp 1818-1824.
Xing,
D & Girolami, M., Employing Latent Dirichlet Allocation for fraud detection in
telecommunications, Pattern
Recognition Letters, 28(13),pp 1818-1824.
Harald Mischak, Rolf Apweiler, Rosamonde E. Banks,
Mark Conaway, Joshua Coon, Anna Dominiczak, Jochen
H. H. Ehrich, Danilo Fliser, Girolami, M., Henning Hermjakob, Denis Hochstrasser, Joachim Jankowski, Bruce A. Julian,
Walter Kolch, Ziad A. Massy, Christian Neusuess, Jan Novak, Karlheinz
Peter, Kasper Rossing, Joost
Schanstra, O. John Semmes, Dan Theodorescu, Visith Thongboonkerd, Eva M. Weissinger,
Jennifer E. Van Eyk, Tadashi Yamamoto., Clinical proteomics: A need to define
the field and to begin to set adequate standards. PROTEOMICS - Clinical Applications, 1(2), pp
148-156.
2006
Girolami, M., Rogers,
S.,
Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors.
Neural Computation,
MIT Press.
Vol. 18, Nos. 8, pp 1790-1817.
Supplementary
Material Bioconductor R Package Available Now
Carrivick,
L., Rogers, S., Clark, J., Campbell,
C., Girolami, M., Cooper, C. Identification
of Prognostic Signatures in Breast Cancer Microarray Data using Bayesian
Techniques. The Journal of the Royal Society Interface.
Vol.3, No.8, pp
367-381.
Szymkowiak-Have, A. Girolami, M.,
Larsen,J. , Clustering
via Kernel Decomposition. IEEE Transactions on Neural Networks Vol.17,No.1,pp.256-264
Kote-Jarai,Z. Matthews,L. Osorio,A. Shanley,S. Giddings,I. Moseews,F. Locke,I. Evans,G. Girolami, M., Williams,R.
Campbell,C., Accurate prediction of BRCA1 and
BRCA2 heterozygous genotype using expression profiling after induced DNA
damage . Clinical Cancer Research .
Vol. 12, No. 13, pp. 3896-3901.
2005
Girolami, M., Rogers,
S.,
Hierarchic Bayesian Models for Kernel Learning. In proc 22nd
International Conference on Machine Learning (ICML 2005), pp 241-248
Supplementary
Material
Rogers, S.,
Girolami, M.,
A Bayesian Regression Approach to the Inference of Regulatory Networks from
Gene Expression. Bioinformatics Vol 21, Nos 14, pp 3131-3137.
Supplementary
Material
Rogers,
S., Girolami, M., Campbell, C.,
& Breitling, R. The Latent
Process Decomposition of cDNA Microarray
Datasets. IEEE/ACM
Transactions on Computational Biology and Bioinformatics
Vol.2, Nos.2, pp 143-156.
Girolami M & Kaban A.Sequential
Activity Profiling : Latent Dirichlet
Allocation of Markov Chains. Data Mining and Knowledge Discovery
Vol 10, 175-196.
2004
Girolami M & Breitling R. Biologically
Valid Linear Factor Models of Gene Expression. Bioinformatics
Vol 20, Nos 17, pp
3021-3033
Supplementary
Material
He,
C., Girolami, M & Ross G. Employing
Optimised Combinations of One-Class Classifiers for Automated Currency
Validation. Pattern Recognition Vol
37, Nos 6 , pp 1085-1096
, Elsevier Science . European
Patent EP1484719 & American Patent US2004247169
He,
C. & Girolami M. Novelty
Detection Employing an L2 Optimal Nonparametric Density Estimator. Pattern
Recognition Letters, Vol 25, Nos 12, pp 1389 - 1397 ,
Elsevier Science.
Girolami,M & Kaban,A.
Simplicial Mixtures of Markov Chains:
Distributed Modelling of Dynamic User Profiles. Advances in Neural
Information Processing Systems 16. pp 9 -- 16, MIT Press
2003
Girolami, M & He, C. Probability
Density Estimation from Optimally Condensed Data Samples. IEEE
Transactions Pattern Analysis and Machine Intelligence, 25(10), 1253-1264 .
Supplementary
Material
2002
Girolami, M. Orthogonal Series Density
Estimation and the Kernel Eigenvalue Problem. Neural
Computation, MIT Press, 14(3), pp 669 - 688. Compressed
PS Format
Girolami, M. Mercer Kernel Based Clustering in Feature Space. IEEE
Transactions on Neural Networks, 13(4), pp 780 - 784.
Compressed
PS Format
Girolami, M. Latent Variable Models for the Topographic
Organisation of Discrete and Strictly Positive Data. Neurocomputing, 48(1-4), pp. 185 - 198.
Kaban,
A. & Girolami, M., A
Dynamic Probabilistic Model to Visualise Topic Evolution in Text Streams,
Journal of Intelligent
Information Systems, 18(2), pp 107-125, 2002.
Special
Issue on Automated Text Categorization, ed's
Thorsten Joachims and Fabrizio Sebastiani
Vinokourov,
A & Girolami, M. A
Probabilistic Framework for the Hierarchic Organisation &Classification
of Document Collections,
Journal of Intelligent Information Systems, 18(2), pp
153-172, 2002.
Special
Issue on Automated Text Categorization. ed's Thorsten Joachims and Fabrizio Sebastiani
Kaban,
A & Girolami, M. Fast
Extraction of Semantic Features From A Latent Semantic Indexed Text Corpus.
Neural Processing Letters, 15(1), pp 31 - 43.
Bingham,
E & Kaban, A, and Girolami, M.
Topic Identification in Chat line Discussions by Extracting Independent
Minimum Complexity Time Components. Neural
Processing Letters , 17, 1-15.
2001
Girolami, M. A Variational
Method for Learning Sparse and Overcomplete
Representations. Neural Computation, MIT Press, 13(11),
pp 2517 - 2532. Compressed
PDF Format,
Compressed PS Format
Girolami, M. The Topographic Organisation and Visualisation of
Binary Data using Mutivariate-Bernoulli Latent
Variable Models. I.E.E.E Transactions on Neural Networks. 12(6).
pp 1367 - 1374.Code
and Data Compressed
PS Format
Kaban,
A & Girolami, M. A
Combined Latent Class and Trait Model for the Analysis and Visualisation of
Discrete Data. I.E.E.E Transactions on Pattern
Analysis and Machine Intelligence. 23(8), pp 859 -.872.
Rosipal,
R.and Girolami, M.
An
Expectation Maximisation Approach to Nonlinear Component Analysis.
Neural Computation, (13), 505-510, MIT Press.
Rosipal,
R., Girolami, M., Trejo, L., Cichocki, A. Kernel
PCA for Feature Extraction and De-Noising in Non-Linear Regression.
Neural Computing & Applications, 10(3), 231-243.
2000
Lee, T, W., Girolami, M., Bell, A, J. & Sejnowski,
T. A
Unifying Information Theoretic Framework for Independent Component
Analysis. International Journal on Mathematical and
Computer Modelling, (39), pp 1-21.
1999
Girolami, M., & Fyfe, C. Stochastic ICA Contrast
Maximisation Using Oja’s Nonlinear PCA Algorithm.
International Journal of Neural Systems, Vol
8, Nos. 5 & 6, pp 661 - 678.
Lee,
T, W., Girolami, M., & Sejnowski, T. Independent
Component Analysis using an Extended Infomax
Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources. Neural
Computation, Vol 11, No2, pp 606-633.
Lee,
T, W., Lewicki, M, S., Girolami, M., Sejnowski, T. Blind
Source Separation of More Sources Using Overcomplete
Representations. IEEE Signal Processing Letters,
Vol.6, No.4. pp 87 – 90.
1998
Girolami, M. An Alternative Perspective
on Adaptive Independent Component Analysis Algorithms. Neural
Computation, Vol 10, No.8, pp 2103 –
2114.
Girolami, M, Cichocki. A., & Amari,
S, I. A Common Neural Network Model for Exploratory Data Analysis and
Independent Component Analysis. IEEE Transactions on Neural Networks,
Vol 9, No.6, pp 1495 - 1501.
Girolami, M. The Latent Variable Data Model for Exploratory Data
Analysis and Visualisation: A Generalisation of the Nonlinear Infomax Algorithm. Neural Processing Letters,Vol 8, No.1, pp 27-39.
Girolami, M. A Nonlinear Model of the Binaural Cocktail Party
Effect. Neurocomputing, Vol 22, No. 1-3, pp 201 – 205.
1997
Girolami, M. Symmetric Adaptive Maximum
Likelihood Estimation for Noise Cancellation and Signal Separation. Electronics
Letters, Vol, 33, No.17, pp 1437 –
1438.
Girolami, M & Fyfe, C. Extraction of Independent Signal
Sources using a Deflationary Exploratory Projection Pursuit Network with
Lateral Inhibition.
I.E.E Proceedings on Vision, Image and Signal Processing , Vol
14, No 5, pp 299 -306.
Girolami, M & Fyfe, C. An Extended Exploratory Projection
Pursuit Network with Linear and Nonlinear Anti-Hebbian
Connections Applied to the Cocktail Party Problem. Neural
Networks, Vol. 10, No. 9, pp. 1607-1618.
1996
Girolami, M & Fyfe, C. A Temporal
Model of Linear Anti-Hebbian Learning.
Neural Processing Letters Journal, Vol
4, No. 3, pp 1-10.
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