Multitask learning: the Bayesian way

author: Tom Heskes, Radboud University Nijmegen
published: Feb. 25, 2007,   recorded: July 2006,   views: 5993


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Multi-task learning lends itself particularly well to a Bayesian approach. Cross-inference between tasks can be implemented by sharing parameters in the likelihood model and the prior for the task-specific model parameters. Choosing different priors, one can implement task clustering and task gating. Throughout my presentation, predicting single-copy newspaper sales will serve as a running example.

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