Semi-Supervised Multi-Task Regression

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An Overview of Multi-task Learning (2018, methodology): “Unlike [63,64], a semi-supervised multi-task regression method is proposed in [65], where each task adopts a Gaussian process and unlabeled data are used to define the kernel function, andGaussian processes in all the tasks share a common prior on kernel parameters.”
A Survey on Multi-Task Learning (2017, background): “learning and active learning can be combined with MTL, leading to three new learning paradigms including semi-supervised multitask learning [117], [118], [119], multi-task active learning [120], [121], [122], and semi-supervised multi-task active learning [123].”
A Convex Feature Learning Formulation for Latent Task Structure Discovery (2012, methodology): “In this section we present our empirical studies on the following benchmark multi-task classification and regression datasets: Sarcos A multiple-output regression dataset used in Zhang & Yeung (2009).”
Efficient Output Kernel Learning for Multiple Tasks (2015, methodology): “Sets We begin with the generalization results in multi-task setups. The data sets are as follows: Sarcos: A multi-task regression data set. The aim is to predict 7 degrees of freedom of a robotic arm [28]. Parkinson: A multi-task regression data set [29] where one needs to predict the Parkinson’s di”