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
207-679 1186

bottom corner

software

  • Three variable interaction tests
    Kernel nonparametric tests for Lancaster three-variable interaction and for total independence. The resulting test statistics are straightforward to compute, and the tests are consistent against all alternatives for a large family of reproducing kernels. The Lancaster test is especially useful where two independent causes individually have weak influence on a third dependent variable, but their combined effect has a strong influence (e.g in finding structure in directed graphical models).
    Matlab code

  • Optimal kernel choice for large-scale two-sample tests
    An optimal kernel selection procedure for the kernel two-sample test. For a given test level (an upper bound on the probability of making a Type I error), the kernel is chosen so as to maximize the test power, and minimize the probability of making a Type II error. The test procedure has cost linear in the sample size, making it suited to data streams.
    Matlab code

  • Kernel Two-Sample Test (updated March 2012)
    A kernel method to perform a statistical test of whether two samples are from different distributions. This test can be applied to high dimensional data, as well as to non-vectorial data such as graphs (i.e., wherever kernels provide a similarity measure).
    Matlab code

  • Kernel Belief Propagation
    A nonparametric approach to learning and inference on trees and graphs with loops, using a kernelized message passing algorithm.
    Matlab code

  • Statistical Independence Tests
    Three different statistical tests of whether two random variables are independent. The test statistics are: a kernel statistic (the Hilbert-Schmidt Independence Criterion), an L1 statistic, and a log-likelihood statistic (the mutual information).
    Matlab code

  • Nonlinear directed acyclic structure learning
    This algorithm learns the structure of a directed graphical model from data, combining a PC style search using nonparametric (kernel) measures of conditional dependence with local searches for additive noise models.
    Matlab code

  • Fast Kernel ICA
    Kernel ICA uses kernel measures of statistical independence to separate linearly mixed sources. We have made this process much faster by using an approximate Newton-like method on the special orthogonal group to perform the optimisation.
    Matlab code

  • Covariate Shift Correction
    Given sets of observations of training and test data, we reweight the training data such that its distribution more closely matches that of the test data. We achieve this goal by matching covariate distributions between training and test sets in a reproducing kernel Hilbert space.
    Matlab code

bottom corner