Mark Girolami

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Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
Ben CalderheadImperial College LondonVerified email at imperial.ac.uk
Simon RogersSenior Lecturer, Computing Science, University of GlasgowVerified email at glasgow.ac.uk
Theodoros (Theo) DamoulasAssociate Professor, University of Warwick - UKRI Turing AI FellowVerified email at warwick.ac.uk
Te-Won LeeVerified email at ucsd.edu
Ata KabanUniversity of Birmingham, School of Computer ScienceVerified email at cs.bham.ac.uk
François-Xavier BriolLecturer in Statistical Science, UCLVerified email at turing.ac.uk
Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoVerified email at salk.edu
Maurizio FilipponeAssociate Professor - EURECOMVerified email at eurecom.fr
Colin CampbellUniversity of BristolVerified email at bris.ac.uk
Vassilios StathopoulosResearch Associate, University College LondonVerified email at ucl.ac.uk
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Roman RosipalSenior Researcher at Slovak Academy of Sciences & CTO at Pacific Development and Technology, LLCVerified email at savba.sk
Harald MischakMosaiques DiagnosticsVerified email at mosaiques.de
Chao HeVP, Head of AI @WisersVerified email at wisers.com
Walter KolchDirector, Systems Biology Ireland, University College DublinVerified email at ucd.ie
Mingjun ZhongDepartment of Computing Science, University of Aberdeen, UKVerified email at abdn.ac.uk
Rainer BreitlingProfessor of Systems Biology, Manchester Institute of Biotechnology, University of ManchesterVerified email at manchester.ac.uk
Vladislav VyshemirskyLecturer, University of StrathclydeVerified email at strath.ac.uk
Andrew M StuartComputing + Mathematical Sciences, CaltechVerified email at caltech.edu

Mark Girolami

University of Cambridge

Verified email at cam.ac.uk - Homepage

Computational Statistics Bayesian Statistics Machine Learning Data Centric Engineering


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Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources TW Lee, M Girolami, TJ Sejnowski Neural computation 11 (2), 417-441, 1999	2235	1999
Riemann manifold langevin and hamiltonian monte carlo methods M Girolami, B Calderhead Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2011	1361	2011
Mercer kernel-based clustering in feature space M Girolami IEEE transactions on neural networks 13 (3), 780-784, 2002	1059	2002
Blind source separation of more sources than mixtures using overcomplete representations TW Lee, MS Lewicki, M Girolami, TJ Sejnowski IEEE signal processing letters 6 (4), 87-90, 1999	619	1999
A unifying information-theoretic framework for independent component analysis TW Lee, M Girolami, AJ Bell, TJ Sejnowski Computers & Mathematics with Applications 39 (11), 1-21, 2000	591	2000
Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease DM Good, P Zürbig, A Argiles, HW Bauer, G Behrens, JJ Coon, M Dakna, ... Molecular & cellular proteomics 9 (11), 2424-2437, 2010	413	2010
Clinical proteomics: a need to define the field and to begin to set adequate standards H Mischak, R Apweiler, RE Banks, M Conaway, J Coon, A Dominiczak, ... PROTEOMICS–Clinical Applications 1 (2), 148-156, 2007	331	2007
Advances in urinary proteome analysis and biomarker discovery D Fliser, J Novak, V Thongboonkerd, À Argilés, V Jankowski, MA Girolami, ... Journal of the American Society of Nephrology 18 (4), 1057-1071, 2007	312	2007
On an equivalence between PLSI and LDA M Girolami, A Kabán Proceedings of the 26th annual international ACM SIGIR conference on …, 2003	287	2003
Probability density estimation from optimally condensed data samples M Girolami, C He IEEE Transactions on pattern analysis and machine intelligence 25 (10), 1253 …, 2003	281	2003
Recommendations for biomarker identification and qualification in clinical proteomics H Mischak, G Allmaier, R Apweiler, T Attwood, M Baumann, A Benigni, ... Science translational medicine 2 (46), 46ps42-46ps42, 2010	274	2010
Hamiltonian Monte Carlo for hierarchical models M Betancourt, M Girolami Current trends in Bayesian methodology with applications 79 (30), 2-4, 2015	270	2015
Variational Bayesian multinomial probit regression with Gaussian process priors M Girolami, S Rogers Neural Computation 18 (8), 1790-1817, 2006	264	2006
Kernel PCA for feature extraction and de-noising in nonlinear regression R Rosipal, M Girolami, LJ Trejo, A Cichocki Neural Computing & Applications 10 (3), 231-243, 2001	245	2001
Sparse multinomial logistic regression via bayesian l1 regularisation GC Cawley, NLC Talbot, M Girolami Advances in neural information processing systems 19, 209, 2007	243	2007
Bayesian ranking of biochemical system models V Vyshemirsky, MA Girolami Bioinformatics 24 (6), 833-839, 2008	240	2008
A first course in machine learning M Girolami Chapman and Hall/CRC, 2011	239	2011
A variational method for learning sparse and overcomplete representations M Girolami Neural computation 13 (11), 2517-2532, 2001	195	2001
Probabilistic numerics and uncertainty in computations P Hennig, MA Osborne, M Girolami Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2015	193	2015
Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection T Damoulas, MA Girolami Bioinformatics 24 (10), 1264-1270, 2008	190	2008

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