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Multitask learning: the Bayesian way
This video was recorded at Open House on Multi-Task and Complex Outputs Learning, London 2006. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
