Dr Robert J. Durrant
Senior Lecturer, Academic Programme Convenor (Statistics)
Qualifications: BSc(Hons) Open; MSc Birm; PhD Birm
Bob joined the Department of Statistics as a lecturer in July 2013, following a short stint as a post-doctoral researcher at the University of Birmingham, UK. He has a BSc(Hons) with first-class honours in Mathematical Sciences, obtained through the Open University while still working in industry, an MSc with distinction in Natural Computation from the University of Birmingham, and a PhD in Computer Science also from Birmingham.
Machine Learning and Statistical Pattern Recognition.
Dimensionality Reduction and Feature Selection.
Learning Theory and Mathematical Statistics, especially non-asymptotic (finite sample) theory.
Random Matrix Theory.
Applications of all of the above. In particular using theory to better understand existing techniques, and to develop efficient, effective, and principled methods for high-dimensional problems with performance guarantees.
Lim, N. J. S., & Durrant, R. J. (2020). A diversity-aware model for majority vote ensemble accuracy. In S. Chiappa, & R. Calandra (Eds.), Proc 23rd International Conference on Artificial Intelligence and Statistics (AISTATS) Vol. 108 (pp. 4078-4086). Electra Network: Addison-Wesley Publ Co. Retrieved from http://gateway.webofknowledge.com/ Open Access version: https://hdl.handle.net/10289/13951
Kaban, A., & Durrant, R. J. (2020). Structure from randomness in halfspace learning with the zero-one loss. Journal of Artificial Intelligence Research, 69, 733-764. doi:10.1613/jair.1.11506 Open Access version: https://hdl.handle.net/10289/13964
Lee, W. S., & Durrant, R. J. (2018). Foreword: Special Issue for the Journal Track of the 9th Asian Conference on Machine Learning (ACML 2017). Machine Learning, 107, 639-641. doi:10.1007/s10994-017-5691-z
Luo, X., & Durrant, R. J. (2018). Maximum gradient dimensionality reduction. In Proceedings - International Conference on Pattern Recognition Vol. 2018-August (pp. 501-506). doi:10.1109/ICPR.2018.8546198 Open Access version: https://hdl.handle.net/10289/13139
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