Gatsby Computational Neuroscience Unit
17 Queen Square
London - WC1N 3AR
+44 (0)20 7679 1176
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.
NIPS 2013 workshop on modern nonparametric methods in machine learning
Kernel Adaptive Metropolis-Hastings and software
Three variable interaction tests and software (to appear in NIPS 2013)
Kernel two-sample tests based on low variance, asymptotically Normal kernel statistics, and software (to appear in NIPS 2013)
Paper to appear in Annals of Statistics, on the relation between energy distance/brownian distance covariance and kernel statistics on probabilities (MMD/HSIC).
Paper in the IEEE Signal Processing Magazine, giving an overview of kernel methods for nonparametric inference in graphical models.
Taxonomic Prediction with Tree-Structured Covariances , ECML/PKDD 2013 ( code )
Hilbert Space Embeddings of Predictive State Representations, UAI 2013
Smooth Operators , ICML 2013