I am a senior lecturer in the Department of Statistics at the University of Waikato, New Zealand. I joined the department on 1st July 2013.
I completed my PhD at the School of Computer Science, University of Birmingham, U.K. where I was supervised by Ata Kaban. My PhD research focused on mitigating issues associated with working with very high-dimensional data by using random projections, and quantifying their impact on classification performance. I submitted my thesis on the 23rd January 2013 and my viva took place on 29th April 2013, when my examiners were John Shawe-Taylor and Peter Tino.
I spent a 4 month stint as a Research Fellow in the School of Computer Science at Birmingham before taking up my current post. Prior to that, in 2008-9 I was a Teaching Fellow in the school and admissions tutor for MSc Natural Computation and MSc Intelligent Systems Engineering.
Before joining academia I had a varied career in industry, most recently as a project manager for a large UK stockbroker.
I am a member of the New Zealand Statistical Association, the Australia and New Zealand chapter of SIGKDD, the IEEE Computer Society, and a professional member of the Royal Society of New Zealand.
My Erdös number is 5.
Robert J. Durrant, Room G.3.31,
Department of Statistics,
University of Waikato,
Private Bag 3105,
e: bobd [at] waikato [dot] ac [dot] nz
t: +64 (0)7 838 4466 x8334
f: +64 (0)7 838 4155
I have a broad interest in learning and vision, both the organic and machine varieties.
I have a particular interest in statistical and computational theories of learning.
My recent research has been into dimensionality reduction. In particular, random projections of very high dimensional data sets into low dimensional spaces, and the effect of projection on classification performance.
Currently I am working on the problem of learning a classifier from a small sample of training data: What properties of data facilitate learning from a small sample, how to learn a stable and effective classifier from a small sample, and better understanding the effect of randomization in the context of classifier ensembles. In particular whether, when, and how, randomization improves generalization when the number of training examples is much smaller than the dimensionality of the data.
I am also interested in the Measure Concentration phenomenon and its application to machine learning and statistical pattern recognition theory and practice. Along similar lines I have some (still largely unexplored) interest in randomized algorithms.
I currently supervise three PhD students and am not able to increase this number at present.
At present I have no funds to finance internships. If you have access to other sources of funding then I am very happy to consider your internship request: Please send me email outlining your interests and including a draft research proposal and transcripts for your degree(s) in pdf format.
For Waikato undergraduate students there are departmental funds available for summer projects to compete for, and I am happy to discuss potential projects and help with drafting a project proposal. Note that for summer projects you should either be reasonably mathematically sophisticated (e.g. as evidenced by good grades in level 2 and 3 maths or stats courses), or an accomplished coder (though this could be in a high-level language such as Matlab, Mathematica or R). Strong candidates will be both, but come and see me anyway if you only meet one of these criteria as I don't rank prospective students solely on academic ability (for example, a "can do" attitude is a major plus).
Dr Antti Puurula. Graduated June 2015. Now a Data Scientist at Telstra.
Dr Varvara Vetrova. Graduated November 2016. Now a Lecturer (Assistant Professor) in Statistics, University of Canterbury, Christchurch.
Xianghui Luo. In progress.
Nick Lim. In progress.
Attaullah Sahito. In progress.
Ata Kaban and R.J. Durrant. Structure-aware error bounds for linear classification with the zero-one loss. Submitted. Arxiv
Xianghui Luo and R.J. Durrant. Maximum Margin Principal Components. Submitted. Arxiv
R.J. Durrant and Ata Kaban. Random Projections as Regularizers: Learning a Linear Discriminant from Fewer Observations than Dimensions. Machine Learning, 99(2), pp 257 – 286, 2015. preprint journal
A. Kaban, J. Bootkrajang, and R.J. Durrant. Towards Large Scale Continuous EDA: A Random Matrix Theory Perspective. (Evolutionary Computation - In Press. doi:10.1162/EVCO_a_00150). preprint journal code
R.J. Durrant and Ata Kaban. A tight bound on the performance of Fisher's linear discriminant in randomly projected data spaces. Pattern Recognition Letters, 33(7), pp 911 – 919, 2012.preprint journal
R.J. Durrant and Ata Kaban. When Is 'Nearest Neighbour' Meaningful: A Converse Theorem and Implications. Journal of Complexity, 25(4), pp 385 – 397, August 2009. preprint journal
M.L. Sanyang, R.J. Durrant and A. Kaban. How effective is Cauchy-EDA in high dimensions? Proceedings IEEE Congress on Evolutionary Computation 2016 (CEC 2016). To Appear. pdf
R.J. Durrant and A. Kaban. Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions. Proceedings 5th Asian Conference on Machine Learning (ACML 2013). JMLR W&CP 29 : pp 17-32, 2013.(Best paper award). pdf supp
A. Kaban and R.J. Durrant. Dimension-adaptive bounds on Compressive FLD Classification. Proceedings 24th International Conference on Algorithmic Learning Theory (ALT 2013 ). LNAI 8139, pp 294-308. Springer. pdf
R.J. Durrant and A. Kaban. Sharp Generalization Error Bounds for Randomly-projected Classifiers. Proceedings 30th International Conference on Machine Learning (ICML 2013). JMLR W&CP 28(3): pp 693-701, 2013. pdf
A. Kaban, J. Bootkrajang, and R.J. Durrant. Towards Large Scale Continuous EDA: A Random Matrix Theory Perspective. Proceedings Genetic and Evolutionary Computation Conference (GECCO 2013), pp 383-390. ACM. (Best paper award in the GDS/EDA track). pdf code
R.J. Durrant and A. Kaban. Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert Space. Proceedings 15th International Conference on Artificial Intelligence and Statistics (AIStats 2012). JMLR W&CP 22: pp 337-345, 2012. pdf
R.J. Durrant and A. Kaban. Compressed Fisher Linear Discriminant Analysis: Classification of Randomly Projected Data. Proceedings 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), pp 1119-1128. ACM. pdf
R.J. Durrant and A. Kaban. A bound on the performance of LDA in randomly projected data spaces. Proceedings 20th International Conference on Pattern Recognition (ICPR 2010), pp 4044-4047. IEEE. (IBM Best Student Paper Award in the Pattern Recognition and Machine Learning track). pdf
A. Kaban and R.J. Durrant. Learning with Lq<1 vs L1-norm regularization with exponentially many irrelevant features. Proceedings 19th European Conference on Machine Learning (ECML08). LNAI 5211, pp. 580-596. Springer. pdf
A. Kaban and R.J. Durrant. A norm-concentration argument for non-convex regularization. ICML/UAI/COLT Workshop on Sparse Optimization and Variable Selection, 9 July, 2008, Helsinki, Finland. Slides (pdf)
R.J. Durrant. Learning in High Dimensions with Projected Linear Discriminants. pdf
R.J. Durrant and Ata Kaban. Flip Probabilities for Random Projections of θ-separated vectors. University of Birmingham, School of Computer Science Technical Report CSR-10-10. pdf
R.J. Durrant and Ata Kaban. A comparison of the moments of a quadratic form involving orthonormalised and normalised random projection matrices. University of Birmingham, School of Computer Science Technical Report CSR-11-04. pdf
Random Projections for Machine Learning and Data Mining: Theory and Applications. ECML-PKDD 2012, Bristol. Slides (pdf) Handouts (6 Slides per page) (pdf)
Learning from Small Sample Sizes. KDD 2015, Sydney. Organiser with Alain C. Vandal. Workshop website
Sparsity in the context of learning from high dimensional data - with Ata Kaban. ICARN International Workshop 26 Sept 2008, Liverpool. pdf
Finite Sample Effects in Compressed Fisher's LDA - with Ata Kaban. 'Breaking News' poster presented at the 13th International Conference on Artificial Intelligence and Statistics (AIStats 2010) Poster (pdf) Proof (pdf)
Random Projections for Dimensionality Reduction. Statistics Seminar, University of Canterbury, Christchurch, 8th June 2017; Statistics Seminar, University College London, 31st August 2017; Computer Science Seminar, Aston University, Birmingham, 7th September 2017; Artificial Intelligence and Natural Computation Seminar, School of Computer Science, University of Birmingham, 11th September 2017; Data, Intelligence and Graphs Group Seminar, Universite Telecom ParisTech, 12th September 2017.
Why is Privacy Hard? Invited talk, Te Punaha Matatini Whanau Retreat, Waitetuna Retreat Centre, 5th October 2017.
Random Projections, Label Flipping and Classification. Statistics Seminar, University of Auckland, 17th February 2016.
The Unreasonable Effectiveness of Random Projections in Computer Science. Invited Talk, 2nd International Workshop on High Dimensional Data Mining at IEEE ICDM 2014, Shenzhen. 14th December 2014.
Random Projections as Regularizers: Learning a Linear Discriminant from Fewer Observations than Dimensions. Statistics Seminar, University of Auckland, 6th August 2014; Departmental Seminar, School of Computer Science, University of Birmingham, 22nd October 2015.
What's your angle? Are most triangles acute or obtuse? Community Open Day Talk, University of Waikato, 17 May 2014.
Random Projections for Data Mining and Optimization: Theory and Applications. NZSA ORSNZ Joint Conference, University of Waikato, 27th November 2013;
Why thousand-dimensional vector spaces are interesting. Mathematics Teachers' Conference, School of Mathematics, University of Birmingham, 7 July 2011.
Random triangles and the curse of dimensionality. Postgraduate seminar, School of Computer Science, University of Birmingham, 2 March 2011; Workshop presentation at 6th-form Mathematics Conference, King Edward's Camp Hill School for Girls, Birmingham, 4 March 2011.
Compressed Fisher Linear Discriminant Analysis: Classification of Randomly Projected Data. Departmental Seminar, School of Computer Science, University of Birmingham, 10 June 2010.
Journal of the Royal Statistical Society: Series B (Wiley)
Transactions on Pattern Analysis and Machine Intelligence (IEEE)
Transactions on Neural Networks and Learning Systems (IEEE)
Transactions on Knowledge and Data Engineering (IEEE)
Transactions on Systems, Man and Cybernetics (IEEE)
IEEE Computational Intelligence Magazine - Special Issue on Computational Intelligence in Big Data
Pattern Analysis and Applications (Springer)
Journal of Classification (Springer)
Pattern Recognition Letters (Elsevier)
Information Sciences (Elsevier)
1st International Workshop on High-dimensional Data Mining at IEEE ICDM 2013
2nd International Workshop on High-dimensional Data Mining at IEEE ICDM 2014
Special session on Label Noise in Classification at ESANN 2014
6th Asian Conference on Machine Learning (ACML 2014)
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015)
7th Asian Conference on Machine Learning (ACML 2015)
8th Asian Conference on Machine Learning (ACML 2016) Program Co-chair with Kee-Eung Kim.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2016)
30th Conference on Neural Information Processing Systems (NIPS 2016)
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017)
31st Conference on Neural Information Processing Systems (NIPS 2017)
27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018)
Last Revised 29th November 2017