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Sensible Priors for Sparse Bayesian Learning

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Journal Club on : “Sensible Priors for Sparse Bayesian Learning”.

Joaquin QuiƱonero Candela; Edward Snelson come to present this and for discussion.

Sparse Bayesian learning suffers from impractical, overconfident predictions where the uncertainty tends to be maximal around the observations. We propose an alternative treatment that breaks the rigidity of the implied prior through decorrelation, and consequently gives reasonable and intuitive error bars. The attractive computational efficiency is retained; learning leads to sparse solutions. An interesting by-product is the ability to model non-stationarity and input-dependent noise.

——— http://research.microsoft.com/research/pubs/view.aspx?tr_id=1364

This talk is part of the Machine Learning Journal Club series.

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