links

Gatsby Computational Neuroscience Unit CSML University College London

Collaborations • I am afflilated with the MPI for Biological Cybernetics as a research scientist.
• I am in collaboration with the Select Lab at CMU.

Contact arthur.gretton@gmail.com
Gatsby Computational Neuroscience Unit
Alexandra House
17 Queen Square
London - WC1N 3AR

Phone
+44 (0)7795 291 705

bottom corner

info

Arthur Gretton I am a lecturer with the Gatsby Computational Neuroscience Unit, part of the Centre for Computational Statistics and Machine Learning at UCL. A short biography.

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.

Recent news

UCL-Duke workshop on Sensing and Analysis of High-Dimensional Data, Sep. 4-5 2014.
• Updated paper on regressing from probability distributions to real numbers. Code.
• Course notes from my April 2014 tutorial at the Nonparametric Measures of Dependence workshop at Columbia. See the teaching page .
Paper in ICML 2014 on Kernel Adaptive Metropolis-Hastings, with experiments on adaptive sampling for pseudo-marginal MCMC. Code.
Paper in ICML 2014 on independence testing for random processes. Code.
Paper in ICML 2014 on Kernel Mean Estimation and Stein Effect.
Paper in AAAI 2014 on Monte Carlo Filtering Using Kernel Embedding of Distributions

Older news

Kernel Bayes' Rule, JMLR, December 2013.
Three variable interaction tests and software (NIPS 2013)
Paper in Annals of Statistics, on the relation between energy distance/brownian distance covariance and kernel statistics on probabilities (MMD/HSIC).

bottom corner