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Prof Arthur Gretton
Gatsby Computational Neuroscience Unit
25 Howland Street
Tel: 020 3108 8122
  • Professor of Machine Learning
  • Gatsby Computational Neurosci Unit
  • Faculty of Life Sciences

Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit, CSML, UCL, which he joined in 2010. He received degrees in physics and systems engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He worked from 2002-2012 at the MPI for Biological Cybernetics, and from 2009-2010 at the Machine Learning Department, Carnegie Mellon University. 
Arthur's research interests include machine learning, kernel methods, statistical learning theory, nonparametric hypothesis testing, blind source separation, Gaussian processes, and non-parametric techniques for neural data analysis. He has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR since April 2013, a member of the NIPS Program Committee in 2008 and 2009, a Senior Area Chair for NIPS in 2018, an Area Chair for ICML in 2011 and 2012, and a member of the COLT Program Committee in 2013. Arthur was co-chair of AISTATS in 2016 (with Christian Robert), and co-tutorials chair of ICML in 2018 (with Ruslan Salakhutdinov).

Research Groups
Research Summary

My current research focus is on using kernel methods to reveal properties and relations in data. A first application is in measuring distances between probability distributions. These distances can be used to determine strength of dependence, for example in measuring how strongly two bodies of text in different languages are related; testing for similarities in two datasets, which can be used in attribute matching for databases (that is, automatically finding which fields of two databases correspond); and testing for conditional dependence, which is useful in detecting redundant variables that carry no additional predictive information, given the variables already observed. I am also working on applications of kernel methods to inference in graphical models, where the relations between variables are learned directly from training data: applications include cross-language document retrieval, depth prediction from still images, and protein configuration prediction.

Teaching Summary

Advanced Topics in Machine Learning: COMP0083 
This course comprises 15 hours on kernel methods (taught by Arthur Gretton), and 15 hours on learning theory (taught by Carlo Ciliberto). The kernel part covers: construction of RKHS, in terms of feature spaces and smoothing properties; simple linear algorithms in RKHS (PCA, ridge regression); kernel methods for hypothesis testing (two-sample, independence); support vector machines for classification, including both the C-SVM and nu-SVM; and further applications of kernels (feature selection, clustering, ICA). There is an additional component (not assessed) on theory of reproducing kernel Hilbert spaces. 

Short course on adaptive modelling 
This course comprises three hours of lectures, and a three hour practical session. Material includes construction of RKHS, in terms of feature spaces and smoothing properties; simple linear algorithms in RKHS (PCA, ridge regression); and support vector machines for classification. 

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