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Bayesian regression and classification with multivariate sparsifying priors

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If you have a question about this talk, please contact Zoubin Ghahramani.

Many regression and classification problems in neuroimaging and bioinformatics belong to the class “large p, small n”: many variables, just a few data points. Popular methods for handling such problems include L1-regularization and spike-and-slab variable selection. These methods are univariate when it comes to determine which variables are selected. In this talk I will present multivariate extensions that allow for the incorporation of (spatio-temporal) constraints and lead to smooth importance maps. I will discuss how to arrive at efficient algorithms for (approximate) inference and will illustrate the methods on fMRI analysis and EEG source localization.

This talk is part of the Machine Learning @ CUED series.

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