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.

I am organising a **workshop** at KDD2015 in Sydney this year on **Learning from Small Sample Sizes** with Alain C. Vandal from AUT. The workshop takes place on Monday 10th August, and **submissions are now open**. The submission deadline is Friday 5th June – please see the workshop webpage for the general call for papers and further details.

Robert J. Durrant, Room G.3.30,

Department of Statistics,

University of Waikato,

Private Bag 3105,

Hamilton 3240

New Zealand

e: bobd [at] waikato [dot] ac [dot] nz

w: http://www.stats.waikato.ac.nz/~bobd/

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.

If you are interested in studying for a PhD with me, then please send me email outlining your interests and including a draft research proposal and transcripts for your degree(s) in pdf format. Note that I would expect you to have a reasonably high level of mathematical sophistication and, ideally, also some coding experience (though this could be in a high-level language such as Matlab, Mathematica or R).

The kind of topics that will catch my eye in your research proposal will be ones that are clearly related to my past or current interests. A non-exhaustive list of the subjects that will catch my eye in your transcript(s) - as indicative of your mathematical ability and capability for abstraction - would include courses on abstract or linear algebra, metric or normed spaces, probability, real or complex analysis or topology.

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.
pdf

A. Kaban, J. Bootkrajang, and R.J. Durrant. **Towards Large Scale Continuous EDA: A Random Matrix
Theory Perspective.**
(Evolutionary Computation - To Appear).
pdf 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.
pdf

R.J.
Durrant and Ata Kaban. **When
Is 'Nearest Neighbor' Meaningful: A Converse Theorem and
Implications.** Journal
of Complexity, 25(4), pp 385 – 397, August 2009. 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)

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)

The Unreasonable Effectiveness of Random Projections in Computer Science. Invited Talk, 2nd International Workshop on High Dimensional Data Mining at IEEE ICDM 2014, Shenzhen. 14 December 2014. Slides (pdf)

Random Projections as Regularizers: Learning a Linear Discriminant from Fewer Observations than Dimensions. Statistics Seminar, University of Auckland, 6th August 2014;

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.

Transactions on Pattern Analysis and Machine Intelligence (IEEE)

Transactions on Neural Networks and Learning Systems (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)

Neurocomputing (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)

Last Revised 20/04/15