Multi-Task Boosting by Exploiting Task Relationships

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An Overview of Multi-task Learning (2018, methodology): “The MTRL method is generalized to multi-task boosting [46] and multi-label learning [47], where each label is treated as a task, and extended to learn sparse task relations in [48].”
A Survey on Multi-Task Learning (2017, background): “Problem (21) has been extended to multi-task boosting [83] and multi-label learning [84] by learning label correlations.”
Smart Multitask Bregman Clustering and Multitask Kernel Clustering (2015, methodology): “The other way is to learn the intertask covariance matrix of the multivariate Gaussian prior which can model both positive and negative task correlations [Fei and Huan 2013; Saha et al. 2011; Zhang and Yeung 2010, 2012a, 2012b, 2014].”
Attribute relation learning for zero-shot classification (2014, methodology): “To be specific, the task covariance matrix C is defined as a parameter matrix for the matrix–variate normal distribution [42] over the model parameters in least square regression or support vector machine (SVM) [43]:”
Transitive Transfer Learning (2015, background): “More recently, approaches that learn the relationships between pairwise tasks are also being developed [12, 27, 28].”