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Expectation Propagation in Sparse Linear Models with Spike and Slab Priors

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Sparse linear models assume that the data have been generated by a linear model whose coefficient vector is sparse: a small number of coefficients take values that are significantly different from zero, while the remaining coefficients are exactly zero. This configuration is especially useful for addressing learning problems with a small number of training instances and a high-dimensional feature space. In a Bayesian approach, sparsity can be favored by using specific priors such as the spike and slab distribution. In this talk, different sparse linear models with spike and slab priors will be analyzed, using expectation propagation for fast approximate inference.

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

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