Home Page of Bob Durrant

About me:

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.





PhD Supervision

Dr Antti Puurula. Graduated June 2015. Now Head of Machine Learning at Vic.ai.

Dr Varvara Vetrova. Graduated November 2016. Now a Lecturer (Assistant Professor) in Statistics, University of Canterbury, Christchurch.

Xianghui Luo. Now an AI Algorithm Engineer at ohmio.

Nick Lim. Graduated February 2020. Now a Post-doc on the TAIAO project..

Attaullah Sahito. In progress.



Publications and Presentations

Journal Papers:

A. Kaban and R.J. Durrant. Structure from Randomness in Halfspace Learning with the Zero-One Loss. Journal of Artificial Intelligence Research (To Appear)

H. M. Gomes, J. Read, A. Bifet and R.J. Durrant. Learning From Evolving Data Streams Through Ensembles of Random Patches. Submitted.

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



Peer-reviewed Conference Papers:

N.J.S. Lim and R.J. Durrant. A Diversity-aware Model for Majority Vote Ensemble Accuracy Proc. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (To Appear).pdf supp

N.J.S. Lim and R.J. Durrant. Defending Deep Nets against Adversarial Images using ‘Pseudosaccades’. (Submitted).pdf supp

X. Luo and R.J. Durrant. Maximum Gradient Dimensionality Reduction. (Proc. 24th International Conference on Pattern Recognition (ICPR 2018).pdf

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).. 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)



PhD Thesis:

R.J. Durrant. Learning in High Dimensions with Projected Linear Discriminants. pdf



Technical Reports:

N.J.S. Lim and R.J. Durrant. Linear Dimensionality Reduction in Linear Time: Johnson-Lindenstrauss-type Guarantees for Random Subspace. ArXiv:1705.06408. X. Luo and R.J. Durrant. Maximum Margin Principal Components. ArXiv:1705.06371.

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



Invited Tutorial:

Random Projections for Machine Learning and Data Mining: Theory and Applications. ECML-PKDD 2012, Bristol. Slides (pdf) Handouts (6 Slides per page) (pdf)



Workshop:

Learning from Small Sample Sizes. KDD 2015, Sydney. Organiser with Alain C. Vandal. Workshop website



Invited Poster Presentation:

Sparsity in the context of learning from high dimensional data - with Ata Kaban. ICARN International Workshop 26 Sept 2008, Liverpool. pdf



Conference Poster:

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)



Talks:

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 Reviewing:

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)

Neurocomputing (Elsevier)

Information Sciences (Elsevier)



Program Committees:

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