Bayesian Multitask Classification With Gaussian Process Priors

By G. Skolidis, G. Sanguinetti. 2011

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Kernels for Vector-Valued Functions: a Review (2011, methodology): “However, the marginal likelihood can be approximated under a Laplace, variational Bayes or expectation propagation (EP) approximation frameworks for multiple output classiﬁcation [93, 11], and used to ﬁnd estimates for the hyperparameters.”
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace (2014, background): “However, most of them [60, 11, 6, 44, 25, 49, 61] have only considered the symmetric multi-task learning, which means that all tasks have been assumed to be of equal importance, whereas our purpose is to enhance performance on a target task given all other source tasks.”
Collaborating between Local and Global Learning for Distributed Online Multiple Tasks (2015, methodology): “Many real world problems include a number of related learning tasks, for which multi-task learning (MTL) [5, 9, 10, 29] methods are favorable, as they can learn multiple related tasks together so as to improve the performance of each task relative to learning them separately.”