Biography

Website: http://theoval.cmp.uea.ac.uk/~gcc/

Follow this link for details of current PhD opportunities in Computing Sciences. But feel free to email me to discuss projects outside these areas and alternative sources of funding.

Academic Background

My principal research interests lie in machine learning, with a particular emphasis on Bayesian and kernel learning methods. I am most interested in theoretical issues and algorithms with a direct impact in the practical application of machine learning techniques, including topics such as feature selection, model selection, performance estimation, model comparison, covariate shift, dealing with imbalanced or "non-standard" data and semi-supervised learning. Most of my applied work centres on problems arising in computational biology, in collaboration with the School of Chemistry and Pharmacy (CAP) and with the nearby John Inness Centre (JIC) and Institute for Food Research (IFR). However I also have long-standing research links with the School of Environmental Sciences (ENV) and the Climatic Research Unit (CRU), working on applications of machine learning in the environmental sciences, particularly on modelling and exploiting predictive uncertainty.

All Publications

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Wainer, J., Cawley, G.

(2017)

Empirical evaluation of resampling procedures for optimising SVM hyperparameters,

in Journal of Machine Learning Research

18

(15)

pp. 1-35

UEA Repository

(Article)

(Published)


Cawley, G., Cowtan, K., Way, R., Jacobs, P., Jokimäki, A.

(2015)

On a minimal model for estimating climate sensitivity,

in Ecological Modelling

297

pp. 20-25

Full Text UEA Repository

(Article)

(Published)


Saeed, A., Cawley, G., Bagnall, A.

(2015)

Benchmarking the Semi-Supervised Naïve Bayes Classifier,

Full Text UEA Repository

(Paper)

(Published)


Guyon, I., Cawley, G., Bennett, K., Jair Escalente, H., Escalera, S., Ho, T. K., Macia, N., Ray, B., Saeed, M., Statnikov, A., Viegas, E.

(2015)

Design of the 2015 ChaLearn AutoML challenge,

in Proceedings of International Joint Conference on Neural Networks (IJCNN).

IEEE Press

Full Text

(Conference contribution)

(Published)


Taylor, D., Cawley, G., Hayward, S.

(2014)

Quantitative method for the assignment of hinge and shear mechanism in protein domain movements,

in Bioinformatics

30

(22)

pp. 3189-3196

Full Text UEA Repository

(Article)

(Published)


Cawley, G., Talbot, N.

(2014)

Kernel learning at the first level of inference,

in Neural Networks

53

pp. 69-80

Full Text UEA Repository

(Article)

(Published)


Taylor, D., Cawley, G., Hayward, S.

(2013)

Classification of Protein Domain Movements using Dynamic Contact Graphs,

in PLoS ONE

8

(11)

article no. e81224

Full Text UEA Repository

(Article)

(Published)


Zhang, G., Ansari, H., Rahman, H., Cawley, G., Hertz, T., Hue, X., Jojic, N., Kim, Y., Kohlbacher, O., Lund, O., Lundegaardi, C., Magaret, C., Nielsen, M., Papadopoulos, H., Raghava, G., Tal, V., Xue, L., Yanover, C., Zhu, S., Rock, M., Crowe, J., Panayiotou, C., Polycarpou, M., Ducho, W., Brusic, V.

(2011)

Machine learning competition in immunology – Prediction of HLA class I binding peptides,

in Journal of Immunological Methods

374

(1-2)

pp. 1-4

Full Text UEA Repository

(Article)

(Published)


Cawley, G.

(2011)

Baseline Methods for Active Learning,

in JMLR: Workshop and Conference Proceedings 16 : Workshop on Active Learning and Experimental Design.

Microtome

pp. 47-57

UEA Repository

(Chapter)

(Published)


Guyon, I., Cawley, G., Dror, G., Lemaire, V.

(2011)

Results of the Active Learning Challenge,

in Workshop on Active Learning and Experimental Design.

Microtome

pp. 19-45

UEA Repository

(Chapter)

(Published)


Cawley, G., Talbot, N.

(2010)

On over-fitting in model selection and subsequent selection bias in performance evaluation,

in Journal of Machine Learning Research

11

pp. 2079-2107

UEA Repository

(Article)

(Published)


Guyon, I., Saffari, A., Dror, G., Cawley, G.

(2010)

Model selection: Beyond the Bayesian/frequentist divide,

in Journal of Machine Learning Research

11

pp. 61-87

UEA Repository

(Article)

(Published)


Cawley, G., Janacek, G.

(2010)

On allometric equations for predicting body mass of dinosaurs,

in Journal of Zoology

280

(4)

pp. 355-361

Full Text UEA Repository

(Article)

(Published)


Cawley, G.

(2010)

Some Baseline Methods for the Active Learning Challenge,

(Poster)

(Published)


Guyon, I., Cawley, G., Dror, G., Lemaire, V.

(2010)

Design and Analysis of the WCCI 2010 Active Learning Challenge,

Full Text UEA Repository

(Paper)

(Published)


Cawley, G. C.

(2009)

Causal & non-causal feature selection for ridge regression,

UEA Repository

(Paper)

(Published)


Bagnall, A., Cawley, G., Whittley, I., Bull, L., Studley, M., Pettipher, M., Tekiner, F.

(2008)

Super Computer Heterogeneous Classifier Meta-Ensembles,

in Data Warehousing and Mining: Concepts, Methodologies, Tools and Applications.

IGI Global

pp. 1320-1333

ISBN 978-1599049519

UEA Repository

(Chapter)

(Published)


Cawley, G., Talbot, N.

(2008)

Efficient approximate leave-one-out cross-validation for kernel logistic regression,

in Machine Learning

71

(2-3)

pp. 243-264

Full Text UEA Repository

(Article)

(Published)


Guyon, I., Saffari, A., Dror, G., Cawley, G.

(2008)

Analysis of the IJCNN 2007 agnostic learning versus prior knowledge challenge,

in Neural Networks

21

(2-3)

pp. 544-550

Full Text UEA Repository

(Article)

(Published)


Theobald, B., Cawley, G., Bangham, A., Matthews, I.

(2008)

Comparing text-driven and speech-driven visual speech synthesisers,

UEA Repository

(Paper)

(Published)


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Key Research Interests

Gavin Cawley is part of the Computational Biology Group and the Knowledge Discovery and Data Mining Group

Gavin's current research interests include a continuation of his post-graduate research on neural networks in speech synthesis, and classification of atmospheric circulation patterns (also using neural networks), in collaboration with Dr Steve Dorling.

 

Selected Publications:

Saadi, K., Talbot, N.L.C., and Cawley, G.C. Optimally regularised kernel Fisher discriminant classification. Neural Networks, Volume 20, Issue 7, Page(s) 832-841, 2007.

Cawley, G. C. and Talbot, N. L. C. Preventing over-fitting during model selection using Bayesian regularisation. Journal of Machine Learning Research, Volume 8, Page(s) 841-861, 2007.

Cawley, G. C. and Talbot, N. L. C. Gene selection in cancer classification using sparse logistic regression with Bayesian regularisation. Bioinformatics, Volume 22, Number 19, Page(s) 2348-2355, 2006.

Cawley, G. C. and Talbot, N. L. C. Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers. Pattern Recognition, Volume 36, Issue 11, Page(s) 2585-2592, 2003.

External Activities and Indicators of Esteem

  • MRC Discipline-hopping Fellowship, 2004
  • Joint Editor, Special Issue of Neurocomputing, 2003 and 2004
  • Co-chair Multi-level Optimisation Workshop at NIPS-2006
  • Co-chaired the workshop on Agnostic Learning versus Prior Knowledge at IJCNN-2007

Key Responsibilities

Chair of Board of Examiners (Postgraduate Teaching)